1 The SAS System                                                                                                    04:16 Friday, September 22, 2017

NOTE: Unable to open SASUSER.REGSTRY. WORK.REGSTRY will be opened instead.
NOTE: All registry changes will be lost at the end of the session.

WARNING: Unable to copy SASUSER registry to WORK registry. Because of this, you will not see registry customizations during this session.
NOTE: Unable to open SASUSER.PROFILE. WORK.PROFILE will be opened instead.
NOTE: All profile changes will be lost at the end of the session.
NOTE: Copyright (c) 2002-2012 by SAS Institute Inc., Cary, NC, USA. 
NOTE: SAS (r) Proprietary Software 9.4 (TS1M2) 
      Licensed to IMS INC/NIH NCI, Site 70086760.
NOTE: This session is executing on the Linux 2.6.32-696.10.2.el6.x86_64 (LIN X64) platform.



NOTE: Updated analytical products:
      
      SAS/STAT 13.2
      SAS/ETS 13.2
      SAS/OR 13.2
      SAS/IML 13.2

NOTE: Additional host information:

 Linux LIN X64 2.6.32-696.10.2.el6.x86_64 #1 SMP Tue Sep 12 14:33:29 UTC 2017 x86_64 CentOS release 6.9 (Final) 


Please contact Rusty Shields or Jason Ashby of IMS, Inc. with questions.

shieldsr@imsweb.com
ashbyj@imsweb.com
301-680-9770



NOTE: SAS initialization used:
      real time           0.07 seconds
      cpu time            0.02 seconds
      
1          /*****************************************************************************/
2          /*****************************************************************************/
3          /* The following files are required to run this example:                     */
4          /*   mixtran_macro_v2.21.sas                                                 */
5          /*   indivint_macro_v2.3.sas                                                 */
6          /*   simulated_example_data.sas7bdat                                         */
7          /*****************************************************************************/
8          /*                                                                           */
9          /*****************************************************************************/
10         /* This example analysis uses regression calibration and fits a logistic     */
11         /* regression model to assess the relationship between a dietary component   */
12         /* and a health outcome.  For this analysis, the example data were simulated */
13         /* to include data from 100,000 participants, and the food frequency         */
14         /* questionnaire (FFQ) is the main dietary instrument.  The dietary          */
15         /* component is red meat, and the health outcome is a binary event variable. */
16         /* The simulated data include a calibration substudy of 1,000 participants   */
17         /* with repeated intake measurements from 24-hour dietary recalls.           */
18         /*                                                                           */
19         /* This analysis uses bootstrap variance estimation, so the univariate       */
20         /* measurement error model and logistic model are fit using the original     */
21         /* data set and 200 replicate data sets.  The following replicfirst and      */
22         /* repliclast macro variables allow specification of a range of replicates.  */
23         /* This example program considers the original data set (i.e. replicate 0).  */
24         /* When the replicfirst macro variable is set to 0, as it is in this         */
25         /* example, the 200 replicate data sets are generated using the SAS          */
2 The SAS System                                                                                                    04:16 Friday, September 22, 2017

26         /* Surveyselect procedure, and these 200 replicate data sets are combined    */
27         /* with the original data set and are saved for later use.                   */
28         /*****************************************************************************/
29         
30         %let replicfirst = 0;
31         %let repliclast  = 0;
32         
33         
34         
35         title1 "Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument";
36         title2 "Predict Intake and Perform Regression Calibration";
37         title3 "Assess Relationship between a Dietary Component and a Health Outcome";
38         
39         
40         
41         ***********************************************************************;
42         *** The path must be modified in the following lines to specify the ***;
43         *** location of the SAS macros, the SAS data files, and the SAS     ***;
44         *** library that will be used for output                            ***;
45         ***********************************************************************;
46         
47         %let home = /prj/dcp/statprog/meas.err/develop.public.resources.stat.meth;
48         
49         *** Include the required macros ***;
50         %include "&home/include.files.macros/mixtran_macro_v2.21.sas";
2167       %include "&home/include.files.macros/indivint_macro_v2.3.sas";
3172       
3173       *** Input data file ***;
3174       libname inlib "&home/data";
NOTE: Libref INLIB was successfully assigned as follows: 
      Engine:        V9 
      Physical Name: /prj/dcp/statprog/meas.err/develop.public.resources.stat.meth/data
3175       
3176       *** Output data library ***;
3177       libname outlib "&home/univar_epidemiology_example5_mle_mainffq/outlib";
NOTE: Libref OUTLIB was successfully assigned as follows: 
      Engine:        V9 
      Physical Name: /prj/dcp/statprog/meas.err/develop.public.resources.stat.meth/univar_epidemiology_example5_mle_mainffq/outlib
3178       
3179       
3180       
3181       ***********************************************************************************;
3182       *** Global macro variable to test for successful execution of the MIXTRAN macro ***;
3183       ***********************************************************************************;
3184       
3185       %global success;
3186       
3187       
3188       
3189       ***********************************************************;
3190       *** Comparison values, t0 and t1, are the 25th and 75th ***;
3191       *** percentiles for each risk model covariate           ***;
3192       ***********************************************************;
3193       
3194       %let t0_redmeat = 46;
3195       %let t1_redmeat = 114;
3196       
3197       %let t0_std_entry_age = -0.8637;
3198       %let t1_std_entry_age =  0.8406;
3199       
3200       %let t0_std_log_bmi = -0.6309;
3201       %let t1_std_log_bmi =  0.6162;
3 The SAS System                                                                                                    04:16 Friday, September 22, 2017

3202       
3203       
3204       
3205       *********************************************************************************;
3206       *** The macro fit_models_replicate_loop is used to call the MIXTRAN and       ***;
3207       *** INDIVINT macros using the original study data (i.e. replicate data set 0) ***;
3208       *** and each of the replicate data sets for bootstrap variance estimation     ***;
3209       *** (i.e. replicate data sets 1, 2, ..., 200)                                 ***;
3210       *********************************************************************************;
3211       
3212       %macro fit_models_replicate_loop;
3213       
3214         **************************************************************************;
3215         *** For replicate 0 create bootstrap data sets and save them to outlib ***;
3216         **************************************************************************;
3217       
3218         %if &replicfirst = 0 %then %do;
3219       
3220           *********************************************************************************;
3221           *** Import the simulated data file for this example and set it as replicate 0 ***;
3222           *********************************************************************************;
3223       
3224           data simdata;
3225             set inlib.simulated_example_data;
3226             replicate = 0;
3227           run;
3228       
3229       
3230       
3231           ***************************************************************************************;
3232           *** Create two data sets that will be the input to proc surveyselect and used to    ***;
3233           *** generate replicate data sets for bootstrap variance estimation.                 ***;
3234           *** One data set includes the calibration substudy data with 24-hour recall values  ***;
3235           *** and FFQ values, and the other data set includes FFQ values and no 24-hour       ***;
3236           *** recall values.                                                                  ***;
3237           ***************************************************************************************;
3238       
3239           data calib_substudy main_study;
3240             set simdata;
3241             if calibration_substudy = 1 then output calib_substudy;
3242             else output main_study;
3243           run;
3244       
3245       
3246       
3247           ********************************************************************************************************;
3248           *** Use proc surveyselect to generate 200 replicate bootstrap data sets for the calibration substudy ***;
3249           ********************************************************************************************************;
3250       
3251           proc surveyselect data=calib_substudy(drop = replicate) out=bootdata_calib_substudy seed=14143 method=urs sampsize=1000 outhits
3251     ! reps=200;
3252           run;
3253       
3254       
3255       
3256           ***************************************************************************************************;
3257           *** Use proc surveyselect to generate 200 replicate bootstrap data sets for the main study data ***;
3258           ***************************************************************************************************;
3259       
3260           proc surveyselect data=main_study(drop = replicate) out=bootdata_main_study seed=63491 method=urs sampsize=99000 outhits reps=200;
3261           run;
3262       
4 The SAS System                                                                                                    04:16 Friday, September 22, 2017

3263       
3264       
3265           ******************************************************;
3266           *** Combine the study data with the bootstrap data ***;
3267           ******************************************************;
3268       
3269           data simdata_and_bootdata;
3270             set simdata bootdata_calib_substudy bootdata_main_study;
3271           run;
3272       
3273       
3274       
3275           ************************************************************************;
3276           *** Create the unique record ID variable for each replicate data set ***;
3277           ************************************************************************;
3278       
3279           proc sort data=simdata_and_bootdata;
3280             by replicate;
3281           run;
3282       
3283           data simdata_and_bootdata;
3284             set simdata_and_bootdata;
3285             by replicate;
3286       
3287             retain replicaterowid;
3288       
3289             if first.replicate then replicaterowid = 1;
3290             else replicaterowid = replicaterowid + 1;
3291           run;
3292       
3293       
3294       
3295           **************************************************;
3296           *** Save the simulated data and bootstrap data ***;
3297           **************************************************;
3298       
3299           data outlib.simdata_and_bootdata(keep = replicate replicaterowid calibration_substudy r1_redmeat_g r2_redmeat_g
3300                                                   std_boxcox_ffq_redmeat_g std_entry_age std_log_bmi ybin_event);
3301             set simdata_and_bootdata;
3302           run;
3303       
3304         %end;
3305       
3306       
3307       
3308         %else %do;
3309       
3310           *********************************************************************************;
3311           *** For other replicates, read in the saved simulated and bootstrap data sets ***;
3312           *********************************************************************************;
3313       
3314           data simdata_and_bootdata;
3315             set outlib.simdata_and_bootdata;
3316           run;
3317       
3318         %end;
3319       
3320       
3321       
3322         %do replicnum = &replicfirst %to &repliclast;
3323       
3324           title4 "Replicate &replicnum";
5 The SAS System                                                                                                    04:16 Friday, September 22, 2017

3325           data select_replicate;
3326             set simdata_and_bootdata (where = (replicate=&replicnum));
3327           run;
3328       
3329           **********************************************************************;
3330           *** Create separate data sets for the MIXTRAN and INDIVINT macros. ***;
3331           *** The input data has one record per person.                      ***;
3332           *** The MIXTRAN macro uses an input data set that includes one or  ***;
3333           *** more observations for each person.                             ***;
3334           *** The INDIVINT macro uses an input data set that includes one    ***;
3335           *** observation for each person.                                   ***;
3336           **********************************************************************;
3337       
3338           data calibmrec(keep = replicaterowid repeat r_redmeat_g std_boxcox_ffq_redmeat_g std_entry_age std_log_bmi)
3339                subj1rec(keep = replicaterowid r1_redmeat_g r2_redmeat_g std_boxcox_ffq_redmeat_g std_entry_age std_log_bmi ybin_event);
3340       
3341             set select_replicate;
3342       
3343             if calibration_substudy = 1 then do;
3344       
3345               ********************************************;
3346               *** Output record for 1st 24-hour recall ***;
3347               ********************************************;
3348       
3349               if r1_redmeat_g >= 0 then do;
3350       
3351                 repeat = 1;
3352       
3353                 r_redmeat_g = r1_redmeat_g;
3354       
3355                 output calibmrec;
3356       
3357               end;
3358       
3359               ********************************************;
3360               *** Output record for 2nd 24-hour recall ***;
3361               ********************************************;
3362       
3363               if r2_redmeat_g >= 0 then do;
3364       
3365                 repeat = 2;
3366       
3367                 r_redmeat_g = r2_redmeat_g;
3368       
3369                 output calibmrec;
3370       
3371               end;
3372       
3373             end;
3374       
3375             ****************************************;
3376             *** Output 1 record for each subject ***;
3377             ****************************************;
3378       
3379             output subj1rec;
3380       
3381           run;
3382       
3383       
3384       
3385           ************************************************************;
3386           *** Calculate the smallest positive 24-hour recall value ***;
6 The SAS System                                                                                                    04:16 Friday, September 22, 2017

3387           ************************************************************;
3388       
3389           proc univariate data=calibmrec noprint;
3390             where r_redmeat_g > 0;
3391             var r_redmeat_g;
3392             output out=outmin_amt min=min_amt;
3393           run;
3394       
3395       
3396       
3397           /*******************************************************************************/
3398           /*******************************************************************************/
3399           /*                                                                             */
3400           /*  Description of the MIXTRAN Macro                                           */
3401           /*                                                                             */
3402           /*******************************************************************************/
3403           /*                                                                             */
3404           /*                                                                             */
3405           /* The MIXTRAN macro is used for the analysis of episodically                  */
3406           /* consumed foods, foods consumed every day, and nutrients, and                */
3407           /* output from the MIXTRAN macro is used by the DISTRIB macro for              */
3408           /* estimation of the distribution of usual intake.                             */
3409           /*                                                                             */
3410           /* For episodically consumed foods, the MIXTRAN macro fits a two-              */
3411           /* part nonlinear mixed model where the first part considers the               */
3412           /* probability of consumption and the second part considers the                */
3413           /* consumption-day amount.  The model allows for covariates and                */
3414           /* includes a random effect in both parts and allows for correlation           */
3415           /* between the random effects (Tooze et al., 2006, Journal of the              */
3416           /* American Dietetic Association, 106, 1575-1587).                             */
3417           /*                                                                             */
3418           /* To fit this nonlinear mixed model with correlated random effects            */
3419           /* (i.e. the correlated model), starting values for the two parts of           */
3420           /* the model are obtained by first using the GENMOD procedure to fit           */
3421           /* a probability model and an amount model.  Then a nonlinear mixed            */
3422           /* model with uncorrelated random effects (i.e. the uncorrelated               */
3423           /* model) is fit using two calls to the NLMIXED procedure, and the             */
3424           /* parameter estimates from this model are used as starting values             */
3425           /* for the correlated model.                                                   */
3426           /*                                                                             */
3427           /* For foods consumed every day and nutrients, the MIXTRAN macro               */
3428           /* uses the GENMOD procedure to calculate starting values and uses             */
3429           /* the NLMIXED procedure to fit an amount-only model.                          */
3430           /*                                                                             */
3431           /* The syntax for calling the macro is:                                        */
3432           /*                                                                             */
3433           /* %mixtran(data=, response=, foodtype=, subject=, repeat=,                    */
3434           /*          covars_prob=, covars_amt=, outlib=, modeltype=,                    */
3435           /*          lambda=, replicate_var=, seq=,                                     */
3436           /*          weekend=, vargroup=, numvargroups=,                                */
3437           /*          start_val1=, start_val2=, start_val3=, vcontrol=,                  */
3438           /*          nloptions=, titles=, printlevel=)                                  */
3439           /*                                                                             */
3440           /*  where                                                                      */
3441           /*                                                                             */
3442           /*   "data"         * Specifies the data set to be used.                       */
3443           /*                                                                             */
3444           /*   "response"     * Specifies the 24-hour recall variable.                   */
3445           /*                                                                             */
3446           /*   "foodtype"     * Specifies a name for the analysis, used to               */
3447           /*                    identify the output data sets.  This value can           */
3448           /*                    be the same as the response variable.                    */
7 The SAS System                                                                                                    04:16 Friday, September 22, 2017

3449           /*                                                                             */
3450           /*   "subject"      * Specifies the variable that uniquely                     */
3451           /*                    identifies each subject.                                 */
3452           /*                                                                             */
3453           /*   "repeat"       * Specifies the variable that indexes repeated             */
3454           /*                    observations for each subject.                           */
3455           /*                                                                             */
3456           /*   "covars_prob"    Specifies a list of covariates for the first             */
3457           /*                    part of the model that models the probability            */
3458           /*                    of consumption.  Covariates must be separated            */
3459           /*                    by spaces.  Interactions must be in the order            */
3460           /*                    specified by PROC GENMOD.  If the model type             */
3461           /*                    is "amount" then covars_prob should be left as           */
3462           /*                    a null string.                                           */
3463           /*                                                                             */
3464           /*   "covars_amt"   * Specifies a list of covariates for the second            */
3465           /*                    part of the model that models the consumption-           */
3466           /*                    day amount.  Covariates must be separated by             */
3467           /*                    spaces.  Interactions must be in the order               */
3468           /*                    specified by PROC GENMOD.                                */
3469           /*                                                                             */
3470           /*   "outlib"       * Specifies a directory where output data sets             */
3471           /*                    are stored.  Outlib can not be null.                     */
3472           /*                                                                             */
3473           /*   "modeltype"    * Specifies the model.  The possible values are:           */
3474           /*                    "null string" = fit correlated model,                    */
3475           /*                    "corr"        = fit correlated model,                    */
3476           /*                    "nocorr"      = fit uncorrelated model,                  */
3477           /*                    "amount"      = fit amount-only model.                   */
3478           /*                                                                             */
3479           /*   "lambda"         Specifies a user supplied value for the                  */
3480           /*                    Box-Cox transformation parameter, lambda.  If            */
3481           /*                    a value is not supplied, the macro will                  */
3482           /*                    calculate a value for lambda.                            */
3483           /*                                                                             */
3484           /*   "replicate_var"  Specifies the variable to be used in the                 */
3485           /*                    replicate statement of PROC NLMIXED or the               */
3486           /*                    freq statement of PROC UNIVARIATE.  The                  */
3487           /*                    specified variable must be integer valued.               */
3488           /*                                                                             */
3489           /*   "seq"            Specifies one or more sequence indicator                 */
3490           /*                    variables to account for effects due to the              */
3491           /*                    sequence number of a subject's records.  This            */
3492           /*                    variable can NOT also appear in covars_prob              */
3493           /*                    or covars_amt.                                           */
3494           /*                                                                             */
3495           /*   "weekend"        Specifies the weekend (Fri.-Sun.) indicator              */
3496           /*                    variable to account for a weekend effect.  A             */
3497           /*                    value of 1 represents a Fri.-Sun. record, and            */
3498           /*                    a value of 0 represents a Mon.-Thurs. record.            */
3499           /*                    This variable can NOT also appear in                     */
3500           /*                    covars_prob or covars_amt.                               */
3501           /*                                                                             */
3502           /*   "vargroup"       Specifies a variable that groups observations            */
3503           /*                    to allow the model to incorporate a separate             */
3504           /*                    residual variance parameter for each of these            */
3505           /*                    groups of observations.  If the output from              */
3506           /*                    this macro is to be used in the DISTRIB macro,           */
3507           /*                    then only the weekend variable can be used.              */
3508           /*                                                                             */
3509           /*   "numvargroups"   Specifies the number of groups defined by the            */
3510           /*                    vargroup variable.  If the output from this              */
8 The SAS System                                                                                                    04:16 Friday, September 22, 2017

3511           /*                    macro is to be used in the DISTRIB macro and             */
3512           /*                    weekend is the "vargroup" variable, then the             */
3513           /*                    number of groups is 2.                                   */
3514           /*                                                                             */
3515           /*   "start_val1"     Starting values for probability model (nocorr).          */
3516           /*                    Use only when vcontrol is called and parameter           */
3517           /*                    estimates (i.e. _parmsf1_"foodtype") from a              */
3518           /*                    previous execution of an analogous model are desired.    */
3519           /*                    Specifies the starting values data set for the           */
3520           /*                    1st PROC NLMIXED (i.e. NLMIXED for probability           */
3521           /*                    model).                                                  */
3522           /*                                                                             */
3523           /*   "start_val2"     Starting values for the amount model.                    */
3524           /*                    Use only when vcontrol is called and parameter           */
3525           /*                    estimates (i.e. _parmsf2_"foodtype") from a              */
3526           /*                    previous execution of an analogous model are desired.    */
3527           /*                    Specifies the starting values data set for the           */
3528           /*                    2nd PROC NLMIXED (i.e. NLMIXED for amount                */
3529           /*                    model).                                                  */
3530           /*                                                                             */
3531           /*   "start_val3"     Starting values for correlated model (corr).             */
3532           /*                    Use only when vcontrol and parameter                     */
3533           /*                    estimates (i.e. _parmsf3_"foodtype") from a              */
3534           /*                    previous execution of an analogous model are desired.    */
3535           /*                    Specifies the starting values data set for the           */
3536           /*                    3rd PROC NLMIXED (i.e. NLMIXED for correlated            */
3537           /*                    model).                                                  */
3538           /*                                                                             */
3539           /*   "vcontrol"       Use only when starting values from a previous            */
3540           /*                    execution of the same model are also used.               */
3541           /*                    Specifies a 1 to 6 character name to                     */
3542           /*                    differentiate output data sets for runs using            */
3543           /*                    the same food.  See the parameters start_val1,           */
3544           /*                    start_val2, and start_val3.  The default is              */
3545           /*                    null.                                                    */
3546           /*                                                                             */
3547           /*   "nloptions"      Specifies a list of options to be added to all           */
3548           /*                    calls to PROC NLMIXED, for example:                      */
3549           /*                       nloptions=qpoints=1 gconv=1e-12 itdetails.            */
3550           /*                                                                             */
3551           /*   "titles"         Specifies the number of title lines (0-4) to             */
3552           /*                    be reserved for the user's titles.  Up to 4              */
3553           /*                    title lines may be reserved for the user's               */
3554           /*                    titles.  The remaining title lines are used by           */
3555           /*                    the macro.  The default value is 0.                      */
3556           /*                                                                             */
3557           /*   "printlevel"     Specifies 1, 2, or 3 to control the amount of            */
3558           /*                    information printed in the list file.                    */
3559           /*                    Printlevel=1 prints only the summary reports.            */
3560           /*                    Printlevel=2 prints summary reports and output           */
3561           /*                    from the NLMIXED procedures.  Printlevel=2 is            */
3562           /*                    the default value.  Printlevel=3 prints                  */
3563           /*                    summary reports and output from all of the               */
3564           /*                    statistical procedures.                                  */
3565           /*                                                                             */
3566           /*                                                                             */
3567           /* Note:  * Parameters marked with an asterisk are mandatory, so a             */
3568           /*          value must be supplied in the macro call.                          */
3569           /*                                                                             */
3570           /* Caution:  variable name "YN" is reserved for this macro.                    */
3571           /*                                                                             */
3572           /* Caution:  data set names "data" and "data0" and "misc_info" are             */
9 The SAS System                                                                                                    04:16 Friday, September 22, 2017

3573           /*           reserved for this macro.                                          */
3574           /*                                                                             */
3575           /*******************************************************************************/
3576       
3577           *****************************************************************;
3578           ***  Call MIXTRAN to fit a nonlinear mixed model for red meat ***;
3579           *****************************************************************;
3580       
3581           %mixtran(data          = calibmrec,
3582                    response      = r_redmeat_g,
3583                    foodtype      = redmeat&replicnum,
3584                    subject       = replicaterowid,
3585                    repeat        = repeat,
3586                    covars_prob   = std_boxcox_ffq_redmeat_g std_entry_age std_log_bmi,
3587                    covars_amt    = std_boxcox_ffq_redmeat_g std_entry_age std_log_bmi,
3588                    outlib        = outlib,
3589                    modeltype     = corr,
3590                    lambda        = ,
3591                    replicate_var = ,
3592                    seq           = ,
3593                    weekend       = ,
3594                    vargroup      = ,
3595                    numvargroups  = ,
3596                    subgroup      = ,
3597                    start_val1    = ,
3598                    start_val2    = ,
3599                    start_val3    = ,
3600                    vcontrol      = ,
3601                    nloptions     = ,
3602                    titles        = 4,
3603                    printlevel    = 1
3604                    );
3605       
3606       
3607       
3608           ********************************************************;
3609           *** If the MIXTRAN macro executed successfully then  ***;
3610           **  continue with this iteration of the example,     ***;
3611           **  otherwise stop this iteration and begin the next ***;
3612           ********************************************************;
3613       
3614           %if &success = 1 %then %do;
3615       
3616             **********************************************************************************;
3617             *** Create an input data set for the INDIVINT macro to predict red meat intake ***;
3618             **********************************************************************************;
3619       
3620             data paramsubj1rec;
3621       
3622               if _n_ = 1 then do;
3623                 set outlib._param_redmeat&replicnum(keep = a01_intercept a02_std_boxcox_ffq_redmeat_g a03_std_entry_age a04_std_log_bmi
3624                                                            p01_intercept p02_std_boxcox_ffq_redmeat_g p03_std_entry_age p04_std_log_bmi
3625                                                            p_var_u1 a_var_u2 cov_u1u2 a_var_e a_lambda);
3626                 set outmin_amt(keep = min_amt);
3627               end;
3628       
3629               set subj1rec(keep = replicaterowid std_boxcox_ffq_redmeat_g std_entry_age std_log_bmi);
3630       
3631               *****************************************************************;
3632               *** Calculate the linear predictor values using the parameter ***;
3633               *** estimates from MIXTRAN and the covariates                 ***;
3634               *****************************************************************;
10 The SAS System                                                                                                   04:16 Friday, September 22, 2017

3635       
3636               x1beta1 = p01_intercept + p02_std_boxcox_ffq_redmeat_g*std_boxcox_ffq_redmeat_g +
3637                         p03_std_entry_age*std_entry_age + p04_std_log_bmi*std_log_bmi;
3638       
3639               x2beta2 = a01_intercept + a02_std_boxcox_ffq_redmeat_g*std_boxcox_ffq_redmeat_g +
3640                         a03_std_entry_age*std_entry_age + p04_std_log_bmi*std_log_bmi;
3641       
3642             run;
3643       
3644       
3645       
3646             /*************************************************************************/
3647             /*************************************************************************/
3648             /*                                                                       */
3649             /*  Description of the INDIVINT Macro                                    */
3650             /*                                                                       */
3651             /*************************************************************************/
3652             /*                                                                       */
3653             /*                                                                       */
3654             /* The INDIVINT macro calculates predicted values for regression         */
3655             /* calibration using methods from Kipnis et al. (Biometrics, 2009) and   */
3656             /* using results from an amount-only model or a two-part model fit using */
3657             /* the MIXTRAN macro.  The INDIVINT macro performs adaptive Gaussian     */
3658             /* quadrature to predict usual intake for each individual, and the macro */
3659             /* allows the user to provide a Box-Cox transformation parameter in      */
3660             /* order to calculate the predicted values on a transformed scale.  The  */
3661             /* results from this macro are intended for use in a subsequent          */
3662             /* regression model as discussed by Kipnis et al. (Biometrics, 2009).    */
3663             /*                                                                       */
3664             /* The syntax for calling the INDIVINT macro is:                         */
3665             /*                                                                       */
3666             /* %indivint(model12=, subj1recdata=, recid=, r24vars=, min_amt=,        */
3667             /*           var_u1=, var_u2=, cov_u1u2=, var_e=, lambda=, xbeta1=,      */
3668             /*           xbeta2=, boxcox_t_lamt=, lamt=, dencalc=, denopt=,          */
3669             /*           u1nlmix=, u2nlmix=, titles=, notesprt=);                    */
3670             /*                                                                       */
3671             /* where the parameters are described as follows.                        */
3672             /*                                                                       */
3673             /*  "model12"            Specifies the type of model that was fit prior  */
3674             /*                       to calling this macro.  A value of 1 indicates  */
3675             /*                       that an amount-only model was fit, and a value  */
3676             /*                       of 2 indicates that a two-part model was fit    */
3677             /*                       where part 1 is the probability part of the     */
3678             /*                       model and part 2 is the amount part of the      */
3679             /*                       model.                                          */
3680             /*                                                                       */
3681             /*  "subj1recdata"       Specifies a data set with 1 record for each     */
3682             /*                       individual.                                     */
3683             /*                                                                       */
3684             /*  "recid"              Specifies an identification (ID) variable that  */
3685             /*                       uniquely identifies each individual's record.   */
3686             /*                                                                       */
3687             /*  "r24vars"            Specifies the 24-hour recall variables with     */
3688             /*                       values that are either non-negative or a SAS    */
3689             /*                       missing value if the 24-hour recall is not      */
3690             /*                       available.  Variables must be space delimited   */
3691             /*                       as illustrated in the following example:        */
3692             /*                       "r24vars=r24hr1 r24hr2".                        */
3693             /*                       Note for Advanced Users:  If all 24-hour recall */
3694             /*                       values are missing for each subject, then the   */
3695             /*                       denominator integration should not be           */
3696             /*                       performed, so the "dencalc" macro parameter     */
11 The SAS System                                                                                                   04:16 Friday, September 22, 2017

3697             /*                       should be specified as "dencalc=n".             */
3698             /*                                                                       */
3699             /*  "min_amt"            Specifies a variable that provides the minimum  */
3700             /*                       intake amount.  This value may be selected as   */
3701             /*                       the smallest value among the observed           */
3702             /*                       consumption-day amounts.  Note that the         */
3703             /*                       specified variable provides the same value for  */
3704             /*                       each individual.  This value will be divided in */
3705             /*                       half and used in the calculations for the       */
3706             /*                       numerator integration.                          */
3707             /*                                                                       */
3708             /*  "var_u1"             Specifies a variable that provides the variance */
3709             /*                       estimate for u1, the random effect from the     */
3710             /*                       probability part of the model.  If a variable   */
3711             /*                       is specified, then the macro will use its value */
3712             /*                       as a diagonal entry of the covariance matrix    */
3713             /*                       which is either a 1x1 matrix or a 2x2 matrix    */
3714             /*                       depending on the number of random effects that  */
3715             /*                       are in the model.                               */
3716             /*                                                                       */
3717             /*  "var_u2"             Specifies a variable that provides the variance */
3718             /*                       estimate for u2, the random effect from the     */
3719             /*                       amount part of the model or from an amount-only */
3720             /*                       model.  If a variable is specified, then the    */
3721             /*                       macro will use its value as a diagonal entry of */
3722             /*                       the covariance matrix which is either a 1x1     */
3723             /*                       matrix or a 2x2 matrix depending on the number  */
3724             /*                       of random effects that are in the model.        */
3725             /*                                                                       */
3726             /*  "cov_u1u2"           Specifies a variable that provides the estimate */
3727             /*                       of the covariance(u1, u2) from the two-part     */
3728             /*                       model.  If the two-part model was an            */
3729             /*                       uncorrelated model, then the specified variable */
3730             /*                       should have a value of zero for every           */
3731             /*                       individual's record.                            */
3732             /*                                                                       */
3733             /*  "var_e"              Specifies a variable that provides the variance */
3734             /*                       estimate for e, the within-person error term    */
3735             /*                       from the amount part of the model or from an    */
3736             /*                       amount-only model.                              */
3737             /*                                                                       */
3738             /*  "lambda"             Specifies a variable that provides the estimate */
3739             /*                       of the Box-Cox parameter, lambda, from the      */
3740             /*                       amount part of the model or from an amount-only */
3741             /*                       model.                                          */
3742             /*                                                                       */
3743             /*  "xbeta1"             Specifies a variable that provides the linear   */
3744             /*                       predictor values calculated using the           */
3745             /*                       covariates and estimates of the fixed effects   */
3746             /*                       parameters from the probability part of the     */
3747             /*                       model.                                          */
3748             /*                                                                       */
3749             /*  "xbeta2"             Specifies a variable that provides the linear   */
3750             /*                       predictor values calculated using the           */
3751             /*                       covariates and estimates of the fixed effects   */
3752             /*                       parameters from the amount part of the model or */
3753             /*                       from an amount-only model.                      */
3754             /*                                                                       */
3755             /*  "boxcox_t_lamt"      If "boxcox_t_lamt=y" or "boxcox_t_lamt=Y" then  */
3756             /*                       individual usual intake will be predicted on a  */
3757             /*                       transformed scale where the Box-Cox             */
3758             /*                       transformation is used with the Box-Cox         */
12 The SAS System                                                                                                   04:16 Friday, September 22, 2017

3759             /*                       parameter value provided by the "lamt" macro    */
3760             /*                       parameter.  The default value for               */
3761             /*                       "boxcox_t_lamt" is "n".                         */
3762             /*                                                                       */
3763             /*  "lamt"               Specifies a variable that provides the Box-Cox  */
3764             /*                       parameter value when "boxcox_t_lamt=y" or       */
3765             /*                       "boxcox_t_lamt=Y".  The macro does not allow    */
3766             /*                       the Box-Cox parameter to be negative.           */
3767             /*                                                                       */
3768             /*  "dencalc"            By default, "dencalc=y" so the denominator      */
3769             /*                       integration is performed.                       */
3770             /*                       Note for Advanced Users:  If all 24-hour recall */
3771             /*                       variables are missing for each subject, then    */
3772             /*                       the denominator integration should not be       */
3773             /*                       performed, so the "dencalc" option should be    */
3774             /*                       specified as "dencalc=n".                       */
3775             /*                                                                       */
3776             /*  "denopt"             By default, "denopt=y" so the denominator       */
3777             /*                       optimization is performed as part of the        */
3778             /*                       denominator integration calculations.           */
3779             /*                       Note for Advanced Users:  In some situations    */
3780             /*                       the denominator optimization is redundant       */
3781             /*                       because the empirical Bayes estimates of u1 and */
3782             /*                       u2 are available from the model fitting         */
3783             /*                       software; therefore, in these situations,       */
3784             /*                       setting "denopt=n" or "denopt=N" allows the     */
3785             /*                       macro to skip this optimization step and use    */
3786             /*                       the variables provided by the "u1nlmix" and     */
3787             /*                       "u2nlmix" macro parameters.                     */
3788             /*                                                                       */
3789             /*  "u1nlmix"            Specifies a variable for an Advanced Users      */
3790             /*                       option.  For details, see the description for   */
3791             /*                       the "denopt" macro parameter.                   */
3792             /*                                                                       */
3793             /*  "u2nlmix"            Specifies a variable for an Advanced Users      */
3794             /*                       option.  For details, see the description for   */
3795             /*                       the "denopt" macro parameter.                   */
3796             /*                                                                       */
3797             /*  "titles"             Specifies the number of title lines to be       */
3798             /*                       reserved for the user's titles.  One additional */
3799             /*                       title line is used by the macro.  The default   */
3800             /*                       value is "0".                                   */
3801             /*                                                                       */
3802             /*  "notesprt"           If "notesprt=n" or "notesprt=N" then notes are  */
3803             /*                       not printed to the SAS log.  The default value  */
3804             /*                       for "notesprt" is "y".                          */
3805             /*                                                                       */
3806             /*************************************************************************/
3807       
3808             *******************************************************;
3809             *** Call INDIVINT to predict the intake of red meat ***;
3810             *******************************************************;
3811       
3812             %indivint(model12       = 2,
3813                       subj1recdata  = paramsubj1rec,
3814                       recid         = replicaterowid,
3815                       r24vars       = r1_redmeat_g r2_redmeat_g,
3816                       min_amt       = min_amt,
3817                       var_u1        = p_var_u1,
3818                       var_u2        = a_var_u2,
3819                       cov_u1u2      = cov_u1u2,
3820                       var_e         = a_var_e,
13 The SAS System                                                                                                   04:16 Friday, September 22, 2017

3821                       lambda        = a_lambda,
3822                       xbeta1        = x1beta1,
3823                       xbeta2        = x2beta2,
3824                       boxcox_t_lamt = ,
3825                       lamt          = ,
3826                       dencalc       = y,
3827                       denopt        = y,
3828                       u1nlmix       = ,
3829                       u2nlmix       = ,
3830                       titles        = 4,
3831                       notesprt      = y
3832                       );
3833       
3834       
3835       
3836             ****************************************************************;
3837             *** Create a data set with the INDIVINT results for red meat ***;
3838             ****************************************************************;
3839       
3840             data subj1recres;
3841               merge subj1rec _resdata(keep = replicaterowid indusint);
3842                 by replicaterowid;
3843               rename indusint = predint_redmeat;
3844             run;
3845       
3846       
3847       
3848             *****************************************************************************************;
3849             *** Standardize the covariates so that one unit equals the distance between t0 and t1 ***;
3850             *****************************************************************************************;
3851       
3852             data subj1recres;
3853               set subj1recres;
3854       
3855               unit_redmeat = &t1_redmeat - &t0_redmeat;
3856               predint_redmeat_unit_iqr = predint_redmeat / unit_redmeat;
3857       
3858               unit_std_entry_age = &t1_std_entry_age - &t0_std_entry_age;
3859               std_entry_age_unit_iqr = std_entry_age / unit_std_entry_age;
3860       
3861               unit_std_log_bmi = &t1_std_log_bmi - &t0_std_log_bmi;
3862               std_log_bmi_unit_iqr = std_log_bmi / unit_std_log_bmi;
3863             run;
3864       
3865       
3866       
3867             ********************************************;
3868             *** Fit a logistic regression risk model ***;
3869             ********************************************;
3870       
3871             proc logistic data=subj1recres outest=regout;
3872               model ybin_event(event='1') = predint_redmeat_unit_iqr std_entry_age_unit_iqr std_log_bmi_unit_iqr;
3873               title5 "Logistic Regression Risk Model: Adjusted for Measurement Error";
3874             run;
3875             title5;
3876       
3877       
3878       
3879             *************************************************************;
3880             *** Save the regression results for the current replicate ***;
3881             *************************************************************;
3882       
14 The SAS System                                                                                                   04:16 Friday, September 22, 2017

3883             data outlib.diethealthout&replicnum(keep = replicate intercept rc_beta_redmeat_unit_iqr rc_or_redmeat_unit_iqr
3884                                                        rc_beta_std_entry_age_unit_iqr rc_or_std_entry_age_unit_iqr
3885                                                        rc_beta_std_log_bmi_unit_iqr rc_or_std_log_bmi_unit_iqr t0_redmeat t1_redmeat
3886                                                        t0_std_entry_age t1_std_entry_age t0_std_log_bmi t1_std_log_bmi);
3887       
3888               set regout(rename = (predint_redmeat_unit_iqr  = rc_beta_redmeat_unit_iqr
3889                                    std_entry_age_unit_iqr    = rc_beta_std_entry_age_unit_iqr
3890                                    std_log_bmi_unit_iqr      = rc_beta_std_log_bmi_unit_iqr
3891                                    ));
3892       
3893               replicate = &replicnum;
3894       
3895       
3896       
3897               *********************************;
3898               *** Calculate the odds ratios ***;
3899               *********************************;
3900       
3901               rc_or_redmeat_unit_iqr       = exp(rc_beta_redmeat_unit_iqr);
3902               rc_or_std_entry_age_unit_iqr = exp(rc_beta_std_entry_age_unit_iqr);
3903               rc_or_std_log_bmi_unit_iqr   = exp(rc_beta_std_log_bmi_unit_iqr);
3904       
3905       
3906       
3907               ****************************************************************;
3908               *** Save the comparison values for each risk model covariate ***;
3909               ****************************************************************;
3910       
3911               t0_redmeat = &t0_redmeat;
3912               t1_redmeat = &t1_redmeat;
3913       
3914               t0_std_entry_age = &t0_std_entry_age;
3915               t1_std_entry_age = &t1_std_entry_age;
3916       
3917               t0_std_log_bmi = &t0_std_log_bmi;
3918               t1_std_log_bmi = &t1_std_log_bmi;
3919       
3920             run;
3921       
3922           ******************************************************************;
3923           *** End of the processing of the INDIVINT macro for iterations ***;
3924           *** that were successful in the MIXTRAN macro                  ***;
3925           ******************************************************************;
3926       
3927           %end;
3928       
3929       
3930       
3931           %else %do;
3932       
3933             ************************************************;
3934             *** Exit the loop if MIXTRAN is unsuccessful ***;
3935             ************************************************;
3936       
3937             %put MIXTRAN was not successful for replicate data set &replicnum - INDIVINT macro was not executed;
3938       
3939           %end;
3940       
3941         %end;   *** End of the replicate loop ***;
3942       
3943       %mend fit_models_replicate_loop;
3944       
15 The SAS System                                                                                                   04:16 Friday, September 22, 2017

3945       
3946       
3947       ************************************************;
3948       *** Call the fit_models_replicate_loop macro ***;
3949       ************************************************;
3950       
3951       %fit_models_replicate_loop;

NOTE: There were 100000 observations read from the data set INLIB.SIMULATED_EXAMPLE_DATA.
NOTE: The data set WORK.SIMDATA has 100000 observations and 29 variables.
NOTE: DATA statement used (Total process time):
      real time           0.22 seconds
      cpu time            0.04 seconds
      


NOTE: There were 100000 observations read from the data set WORK.SIMDATA.
NOTE: The data set WORK.CALIB_SUBSTUDY has 1000 observations and 29 variables.
NOTE: The data set WORK.MAIN_STUDY has 99000 observations and 29 variables.
NOTE: DATA statement used (Total process time):
      real time           0.02 seconds
      cpu time            0.03 seconds
      


NOTE: The data set WORK.BOOTDATA_CALIB_SUBSTUDY has 200000 observations and 30 variables.
NOTE: The PROCEDURE SURVEYSELECT printed page 1.
NOTE: PROCEDURE SURVEYSELECT used (Total process time):
      real time           0.12 seconds
      cpu time            0.11 seconds
      


NOTE: The data set WORK.BOOTDATA_MAIN_STUDY has 19800000 observations and 30 variables.
NOTE: The PROCEDURE SURVEYSELECT printed page 2.
NOTE: PROCEDURE SURVEYSELECT used (Total process time):
      real time           9.38 seconds
      cpu time            9.38 seconds
      


NOTE: There were 100000 observations read from the data set WORK.SIMDATA.
NOTE: There were 200000 observations read from the data set WORK.BOOTDATA_CALIB_SUBSTUDY.
NOTE: There were 19800000 observations read from the data set WORK.BOOTDATA_MAIN_STUDY.
NOTE: The data set WORK.SIMDATA_AND_BOOTDATA has 20100000 observations and 30 variables.
NOTE: DATA statement used (Total process time):
      real time           5.65 seconds
      cpu time            5.46 seconds
      


NOTE: There were 20100000 observations read from the data set WORK.SIMDATA_AND_BOOTDATA.
NOTE: The data set WORK.SIMDATA_AND_BOOTDATA has 20100000 observations and 30 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           34.41 seconds
      cpu time            26.58 seconds
      


NOTE: There were 20100000 observations read from the data set WORK.SIMDATA_AND_BOOTDATA.
NOTE: The data set WORK.SIMDATA_AND_BOOTDATA has 20100000 observations and 31 variables.
NOTE: DATA statement used (Total process time):
16 The SAS System                                                                                                   04:16 Friday, September 22, 2017

      real time           16.77 seconds
      cpu time            8.52 seconds
      


NOTE: There were 20100000 observations read from the data set WORK.SIMDATA_AND_BOOTDATA.
NOTE: The data set OUTLIB.SIMDATA_AND_BOOTDATA has 20100000 observations and 9 variables.
NOTE: DATA statement used (Total process time):
      real time           15.31 seconds
      cpu time            3.25 seconds
      


NOTE: There were 100000 observations read from the data set WORK.SIMDATA_AND_BOOTDATA.
      WHERE replicate=0;
NOTE: The data set WORK.SELECT_REPLICATE has 100000 observations and 31 variables.
NOTE: DATA statement used (Total process time):
      real time           1.33 seconds
      cpu time            1.32 seconds
      


NOTE: There were 100000 observations read from the data set WORK.SELECT_REPLICATE.
NOTE: The data set WORK.CALIBMREC has 2000 observations and 6 variables.
NOTE: The data set WORK.SUBJ1REC has 100000 observations and 7 variables.
NOTE: DATA statement used (Total process time):
      real time           0.02 seconds
      cpu time            0.04 seconds
      


NOTE: The data set WORK.OUTMIN_AMT has 1 observations and 1 variables.
NOTE: PROCEDURE UNIVARIATE used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      

## Code for correlated model will be executed

NOTE: There were 2000 observations read from the data set WORK.CALIBMREC.
NOTE: The data set WORK.DATA has 2000 observations and 8 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.DATA.
NOTE: The data set WORK.DATA has 2000 observations and 8 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.DATA.
NOTE: The data set WORK.DATA0 has 2000 observations and 8 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      

17 The SAS System                                                                                                   04:16 Friday, September 22, 2017


NOTE: There were 2000 observations read from the data set WORK.DATA0.
NOTE: The data set WORK._MXT_RECALLS has 1000 observations and 5 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1000 observations read from the data set WORK._MXT_RECALLS.
NOTE: The data set WORK._MXT_RECALLS_COUNT has 3 observations and 3 variables.
NOTE: PROCEDURE FREQ used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1000 observations read from the data set WORK._MXT_RECALLS.
NOTE: The data set WORK._MXT_RECALLS_PERCENT has 3 observations and 3 variables.
NOTE: PROCEDURE FREQ used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 3 observations read from the data set WORK._MXT_RECALLS_COUNT.
NOTE: The data set WORK._MXT_RECALLS_COUNT has 3 observations and 3 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 3 observations read from the data set WORK._MXT_RECALLS_PERCENT.
NOTE: The data set WORK._MXT_RECALLS_PERCENT has 3 observations and 3 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      

NOTE: There were 3 observations read from the data set WORK._MXT_RECALLS_COUNT.
NOTE: There were 3 observations read from the data set WORK._MXT_RECALLS_PERCENT.
NOTE: The data set WORK._MXT_RECALLS has 3 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 3 observations read from the data set WORK._MXT_RECALLS.
NOTE: The PROCEDURE PRINT printed page 3.
NOTE: PROCEDURE PRINT used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      

NOTE: PROCEDURE SQL used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      

The number of subjects with two or more positive responses is      561
18 The SAS System                                                                                                   04:16 Friday, September 22, 2017


NOTE: Deleting WORK._MXT_RECALLS (memtype=DATA).
NOTE: Deleting WORK._MXT_RECALLS_COUNT (memtype=DATA).
NOTE: Deleting WORK._MXT_RECALLS_PERCENT (memtype=DATA).

NOTE: PROCEDURE DATASETS used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: The data set WORK.MISC_INFO has 1 observations and 1 variables.
NOTE: PROCEDURE UNIVARIATE used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.MISC_INFO.
NOTE: The data set WORK.MISC_INFO has 1 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.DATA.
NOTE: 1000 observations with duplicate key values were deleted.
NOTE: The data set WORK._PERSONS has 1000 observations and 8 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      

NOTE: There were 1000 observations read from the data set WORK._PERSONS.
NOTE: The data set WORK._PERSONS has 1000 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      



NOTE: PROC GENMOD is modeling the probability that YN='1'.
NOTE: The Pearson chi-square and deviance are not computed since the AGGREGATE option is not specified.
NOTE: Algorithm converged.
NOTE: The scale parameter was held fixed.
NOTE: The data set WORK.MODELFITB has 5 observations and 4 variables.
NOTE: The data set WORK.PARMSG1 has 5 observations and 8 variables.
NOTE: PROCEDURE GENMOD used (Total process time):
      real time           0.02 seconds
      cpu time            0.02 seconds
      


NOTE: The data set WORK.RANDOM1 has 1 observations and 3 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


19 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: There were 5 observations read from the data set WORK.PARMSG1.
NOTE: The data set WORK.NEWNAMES1 has 4 observations and 10 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 4 observations read from the data set WORK.NEWNAMES1.
NOTE: There were 1 observations read from the data set WORK.RANDOM1.
NOTE: The data set WORK.START1 has 5 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.START1.
NOTE: The data set WORK.TRANS1 has 4 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 4 observations read from the data set WORK.TRANS1.
NOTE: The data set WORK.OUT1 has 1 observations and 5 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.OUT1.
NOTE: The data set WORK.EQN1 has 1 observations and 7 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.START1.
NOTE: The data set WORK.START1VARGRP has 5 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: Convergence criterion (GCONV=1E-8) satisfied.
NOTE: The data set WORK.CONVF1 has 1 observations and 2 variables.
NOTE: The data set WORK.FITF1 has 4 observations and 2 variables.
NOTE: The data set WORK.ADPRMSF1 has 1 observations and 9 variables.
NOTE: The data set WORK.PARMSF1 has 5 observations and 10 variables.
NOTE: The data set WORK.PREDU1U has 1000 observations and 10 variables.
NOTE: The data set WORK.PREDPU has 2000 observations and 16 variables.
NOTE: The data set WORK.PREDX1B1U has 2000 observations and 16 variables.
NOTE: PROCEDURE NLMIXED used (Total process time):
      real time           1.16 seconds
      cpu time            1.16 seconds
      

20 The SAS System                                                                                                   04:16 Friday, September 22, 2017


NOTE: There were 1 observations read from the data set WORK.CONVF1.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.ADPRMSF1.
NOTE: The data set WORK.ADPRMSF1 has 1 observations and 1 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      


NOTE: The data set WORK.BOXCOX has 20 observations and 7 variables.
NOTE: There were 1421 observations read from the data set WORK.DATA0.
      WHERE r_redmeat_g>0;
NOTE: PROCEDURE TRANSREG used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column).
      3951:63   
NOTE: There were 20 observations read from the data set WORK.BOXCOX.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: Missing values were generated as a result of performing an operation on missing values.
      Each place is given by: (Number of times) at (Line):(Column).
      579 at 3951:37   579 at 3951:1    
NOTE: There were 2000 observations read from the data set WORK.DATA0.
NOTE: The data set WORK.DATA0 has 2000 observations and 9 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      



NOTE: Algorithm converged.
NOTE: The scale parameter was estimated by maximum likelihood.
NOTE: The data set WORK.PARMSG2 has 5 observations and 8 variables.
NOTE: PROCEDURE GENMOD used (Total process time):
      real time           0.01 seconds
      cpu time            0.01 seconds
      


NOTE: The data set WORK.RANDOM2 has 1 observations and 3 variables.
21 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      


NOTE: There were 5 observations read from the data set WORK.PARMSG2.
NOTE: The data set WORK.NEWNAMES2 has 5 observations and 10 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: The data set WORK.LAMBDA has 1 observations and 3 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.NEWNAMES2.
NOTE: There were 1 observations read from the data set WORK.RANDOM2.
NOTE: There were 1 observations read from the data set WORK.LAMBDA.
NOTE: The data set WORK.START2 has 7 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 7 observations read from the data set WORK.START2.
NOTE: The data set WORK.TRANS2 has 5 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.TRANS2.
NOTE: The data set WORK.OUT2 has 1 observations and 6 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.OUT2.
NOTE: The data set WORK.EQN2 has 1 observations and 8 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      

NOTE: There were 7 observations read from the data set WORK.START2.
NOTE: The data set WORK.START2VARGRP has 7 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


22 The SAS System                                                                                                   04:16 Friday, September 22, 2017


NOTE: Convergence criterion (GCONV=1E-8) satisfied.
NOTE: At least one element of the gradient is greater than 1e-3.
NOTE: The data set WORK.CONVF2 has 1 observations and 2 variables.
NOTE: The data set WORK.FITF2 has 4 observations and 2 variables.
NOTE: The data set WORK.ADPRMSF2 has 2 observations and 9 variables.
NOTE: The data set WORK.PARMSF2 has 7 observations and 10 variables.
NOTE: The data set WORK.PREDU2U has 1000 observations and 10 variables.
NOTE: The data set WORK.PREDEINTU has 2000 observations and 17 variables.
NOTE: The data set WORK.PREDX2B2U has 2000 observations and 17 variables.
NOTE: PROCEDURE NLMIXED used (Total process time):
      real time           0.42 seconds
      cpu time            0.41 seconds
      


NOTE: There were 1 observations read from the data set WORK.CONVF2.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      


NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2 observations read from the data set WORK.ADPRMSF2.
NOTE: The data set WORK.ADPRMSF2 has 1 observations and 2 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.PARMSF1.
NOTE: There were 7 observations read from the data set WORK.PARMSF2.
NOTE: The data set WORK.COV has 2 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2 observations read from the data set WORK.COV.
NOTE: The data set WORK.OUTCOV has 1 observations and 3 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.OUTCOV.
NOTE: The data set WORK.COV2 has 1 observations and 3 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


23 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: There were 4 observations read from the data set WORK.FITF1.
NOTE: The data set WORK.FIT1 has 2 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2 observations read from the data set WORK.FIT1.
NOTE: The data set WORK.FIT1 has 2 observations and 4 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 4 observations read from the data set WORK.FITF2.
NOTE: The data set WORK.FIT2 has 2 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2 observations read from the data set WORK.FIT2.
NOTE: The data set WORK.FIT2 has 2 observations and 4 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2 observations read from the data set WORK.FIT1.
NOTE: There were 2 observations read from the data set WORK.FIT2.
NOTE: The data set WORK.FITBOTH has 2 observations and 6 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.START1VARGRP.
NOTE: There were 7 observations read from the data set WORK.START2VARGRP.
NOTE: There were 1 observations read from the data set WORK.COV2.
NOTE: There were 2 observations read from the data set WORK.FIT1.
NOTE: There were 2 observations read from the data set WORK.FIT2.
NOTE: The data set WORK.NAMES has 17 observations and 5 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 17 observations read from the data set WORK.NAMES.
NOTE: The data set WORK.NAMES has 17 observations and 5 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 17 observations read from the data set WORK.NAMES.
24 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: The data set WORK.NAMES2 has 13 observations and 5 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.PARMSF1.
NOTE: There were 7 observations read from the data set WORK.PARMSF2.
NOTE: There were 1 observations read from the data set WORK.COV2.
NOTE: The data set WORK.START3 has 13 observations and 11 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.MISC_INFO.
NOTE: There were 1 observations read from the data set WORK.ADPRMSF1.
NOTE: There were 1 observations read from the data set WORK.ADPRMSF2.
NOTE: The data set WORK.MISC_INFO has 1 observations and 8 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK.START3.
NOTE: The data set WORK.START3 has 13 observations and 11 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK.NAMES2.
NOTE: There were 13 observations read from the data set WORK.START3.
NOTE: The data set WORK.STARTM has 13 observations and 14 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      


NOTE: There were 17 observations read from the data set WORK.NAMES.
NOTE: There were 13 observations read from the data set WORK.START3.
NOTE: The data set WORK.ESTPRINT has 17 observations and 14 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 17 observations read from the data set WORK.ESTPRINT.
NOTE: The data set WORK.ESTPRINT has 17 observations and 14 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.EQN2.
25 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: The data set OUTLIB.ETAS_REDMEAT0 has 1 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.05 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set OUTLIB.ETAS_REDMEAT0.
NOTE: There were 1 observations read from the data set WORK.EQN1.
NOTE: The data set OUTLIB.ETAS_REDMEAT0 has 1 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.01 seconds
      cpu time            0.00 seconds
      


NOTE: There were 5 observations read from the data set WORK.PARMSF1.
NOTE: The data set OUTLIB._PARMSF1_REDMEAT0 has 5 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.02 seconds
      cpu time            0.00 seconds
      


NOTE: There were 7 observations read from the data set WORK.PARMSF2.
NOTE: The data set OUTLIB._PARMSF2_REDMEAT0 has 7 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.03 seconds
      cpu time            0.01 seconds
      


NOTE: There were 13 observations read from the data set WORK.START3.
NOTE: The data set WORK._PARAM_UNC has 13 observations and 11 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK._PARAM_UNC.
NOTE: The data set OUTLIB._PARAM_UNC_REDMEAT0 has 1 observations and 13 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.02 seconds
      cpu time            0.01 seconds
      


NOTE: There were 1 observations read from the data set OUTLIB._PARAM_UNC_REDMEAT0.
NOTE: There were 1 observations read from the data set WORK.MISC_INFO.
NOTE: The data set OUTLIB._PARAM_UNC_REDMEAT0 has 1 observations and 21 variables.
NOTE: DATA statement used (Total process time):
      real time           0.01 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.PREDX2B2U.
NOTE: The data set WORK.PREDX2B2U has 2000 observations and 17 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
26 The SAS System                                                                                                   04:16 Friday, September 22, 2017

      


NOTE: There were 2000 observations read from the data set WORK.PREDX2B2U.
NOTE: There were 1000 observations read from the data set WORK._PERSONS.
NOTE: The data set WORK.PREDX2B2U has 2000 observations and 18 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.PREDX1B1U.
NOTE: There were 2000 observations read from the data set WORK.PREDX2B2U.
NOTE: The data set OUTLIB._PRED_UNC_REDMEAT0 has 1000 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.02 seconds
      cpu time            0.00 seconds
      

## the data sets outlib._pred_unc_redmeat0 and outlib._param_unc_redmeat0
## have been output for use in the DISTRIB macro or other software using AMOUNT or NOCORR models

NOTE: There were 13 observations read from the data set WORK.STARTM.
NOTE: The data set WORK.STARTM has 13 observations and 14 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      



NOTE: Convergence criterion (GCONV=1E-8) satisfied.
NOTE: At least one element of the gradient is greater than 1e-3.
NOTE: The data set WORK.CONVF3 has 1 observations and 2 variables.
NOTE: The data set WORK.FITF3 has 4 observations and 2 variables.
NOTE: The data set WORK.ADPRMSF3 has 5 observations and 9 variables.
NOTE: The data set WORK.PARMSF3 has 13 observations and 10 variables.
NOTE: The data set WORK.RNDM has 2000 observations and 10 variables.
NOTE: The data set WORK.PREDP has 2000 observations and 16 variables.
NOTE: The data set WORK.PREDEINT has 2000 observations and 16 variables.
NOTE: The data set WORK.PREDX2B2 has 2000 observations and 16 variables.
NOTE: The data set WORK.PREDX1B1 has 2000 observations and 16 variables.
NOTE: The data set WORK._PREDU1 has 2000 observations and 16 variables.
NOTE: The data set WORK._PREDU2 has 2000 observations and 16 variables.
NOTE: PROCEDURE NLMIXED used (Total process time):
      real time           24.64 seconds
      cpu time            24.66 seconds
      


NOTE: There were 1 observations read from the data set WORK.CONVF3.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      
27 The SAS System                                                                                                   04:16 Friday, September 22, 2017



NOTE: There were 5 observations read from the data set WORK.ADPRMSF3.
NOTE: The data set WORK.ADPRMSF3 has 1 observations and 5 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.MISC_INFO.
NOTE: There were 1 observations read from the data set WORK.ADPRMSF3.
NOTE: The data set WORK.MISC_INFO has 1 observations and 9 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      


NOTE: There were 2000 observations read from the data set WORK.RNDM.
NOTE: The data set WORK.PREDU1 has 1000 observations and 10 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.RNDM.
NOTE: The data set WORK.PREDU2 has 1000 observations and 10 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.CONVF3.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK.PARMSF3.
NOTE: The data set OUTLIB._PARMSF3_REDMEAT0 has 13 observations and 2 variables.
NOTE: DATA statement used (Total process time):
      real time           0.04 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK.PARMSF3.
NOTE: The data set WORK._PARAM has 13 observations and 10 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      
28 The SAS System                                                                                                   04:16 Friday, September 22, 2017



NOTE: There were 13 observations read from the data set WORK._PARAM.
NOTE: The data set OUTLIB._PARAM_REDMEAT0 has 1 observations and 13 variables.
NOTE: PROCEDURE TRANSPOSE used (Total process time):
      real time           0.04 seconds
      cpu time            0.01 seconds
      


NOTE: There were 1 observations read from the data set OUTLIB._PARAM_REDMEAT0.
NOTE: There were 1 observations read from the data set WORK.MISC_INFO.
NOTE: The data set OUTLIB._PARAM_REDMEAT0 has 1 observations and 22 variables.
NOTE: DATA statement used (Total process time):
      real time           0.01 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.PREDX2B2.
NOTE: The data set WORK.PREDX2B2 has 2000 observations and 16 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.PREDX2B2.
NOTE: There were 1000 observations read from the data set WORK._PERSONS.
NOTE: The data set WORK.PREDX2B2 has 2000 observations and 17 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2000 observations read from the data set WORK.PREDX1B1.
NOTE: There were 2000 observations read from the data set WORK.PREDX2B2.
NOTE: The data set OUTLIB._PRED_REDMEAT0 has 1000 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.03 seconds
      cpu time            0.01 seconds
      

## the data sets outlib._pred_redmeat0 and outlib._param_redmeat0
## have been output for use in the DISTRIB macro or other software using CORR models

NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 4 observations read from the data set WORK.FITF3.
NOTE: The data set WORK.FIT3 has 2 observations and 5 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK.STARTM.
29 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: There were 2 observations read from the data set WORK.FIT3.
NOTE: The data set WORK.NAMESF has 15 observations and 4 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 15 observations read from the data set WORK.NAMESF.
NOTE: The data set WORK.NAMESF has 15 observations and 4 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK.PARMSF3.
NOTE: The data set WORK.PARMSF3 has 13 observations and 10 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 2 observations read from the data set WORK.FITBOTH.
NOTE: The data set WORK.FITBOTH has 2 observations and 6 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 13 observations read from the data set WORK.PARMSF3.
NOTE: There were 15 observations read from the data set WORK.NAMESF.
NOTE: There were 2 observations read from the data set WORK.FITBOTH.
NOTE: The data set WORK.FINAL has 15 observations and 20 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 15 observations read from the data set WORK.FINAL.
NOTE: The data set WORK.FINAL has 15 observations and 20 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 15 observations read from the data set WORK.FINAL.
NOTE: The data set WORK.LL has 1 observations and 1 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 15 observations read from the data set WORK.FINAL.
NOTE: The data set WORK.AIC has 1 observations and 1 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
30 The SAS System                                                                                                   04:16 Friday, September 22, 2017

      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.LL.
NOTE: There were 1 observations read from the data set WORK.AIC.
NOTE: The data set WORK.NUMPARMS has 1 observations and 6 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: 21 lines were written to file PRINT.
NOTE: There were 17 observations read from the data set WORK.ESTPRINT.
NOTE: The DATA step printed page 4.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: Missing values were generated as a result of performing an operation on missing values.
      Each place is given by: (Number of times) at (Line):(Column).
      13 at 3951:11   
NOTE: There were 17 observations read from the data set WORK.ESTPRINT.
NOTE: The data set WORK.ESTPRINT2 has 17 observations and 16 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: 8 lines were written to file PRINT.
NOTE: There were 17 observations read from the data set WORK.ESTPRINT2.
NOTE: The DATA step printed page 5.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      


NOTE: 17 lines were written to file PRINT.
NOTE: There were 15 observations read from the data set WORK.FINAL.
NOTE: The DATA step printed page 6.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: 6 lines were written to file PRINT.
NOTE: There were 15 observations read from the data set WORK.FINAL.
NOTE: The DATA step printed page 7.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 15 observations read from the data set WORK.FINAL.
NOTE: The data set WORK.FINAL has 15 observations and 20 variables.
31 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 1 observations read from the data set WORK.NUMPARMS.
NOTE: The data set WORK.NUMPARMS has 1 observations and 6 variables.
NOTE: PROCEDURE SORT used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 15 observations read from the data set WORK.FINAL.
NOTE: There were 1 observations read from the data set WORK.NUMPARMS.
NOTE: The data set WORK.FINALLL has 3 observations and 26 variables.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: Deleting WORK.MISC_INFO (memtype=DATA).
NOTE: Deleting WORK._PERSONS (memtype=DATA).
NOTE: Deleting WORK.DATA (memtype=DATA).
NOTE: Deleting WORK.DATA0 (memtype=DATA).
NOTE: Deleting WORK.FITF3 (memtype=DATA).
NOTE: Deleting WORK.PARMSF3 (memtype=DATA).
NOTE: Deleting WORK.CONVF3 (memtype=DATA).
NOTE: Deleting WORK.PREDEINT (memtype=DATA).
NOTE: Deleting WORK.PREDP (memtype=DATA).
NOTE: Deleting WORK.PREDU1 (memtype=DATA).
NOTE: Deleting WORK.PREDU2 (memtype=DATA).
NOTE: Deleting WORK.PREDX1B1 (memtype=DATA).
NOTE: Deleting WORK.PREDX2B2 (memtype=DATA).
NOTE: Deleting WORK._PREDU1 (memtype=DATA).
NOTE: Deleting WORK._PREDU2 (memtype=DATA).
NOTE: Deleting WORK.BOXCOX (memtype=DATA).
NOTE: Deleting WORK.CONVF1 (memtype=DATA).
NOTE: Deleting WORK.CONVF2 (memtype=DATA).
NOTE: Deleting WORK.COV (memtype=DATA).
NOTE: Deleting WORK.COV2 (memtype=DATA).
NOTE: Deleting WORK.EQN1 (memtype=DATA).
NOTE: Deleting WORK.EQN2 (memtype=DATA).
NOTE: Deleting WORK.ESTPRINT (memtype=DATA).
NOTE: Deleting WORK.ESTPRINT2 (memtype=DATA).
NOTE: Deleting WORK.FIT1 (memtype=DATA).
NOTE: Deleting WORK.FIT2 (memtype=DATA).
NOTE: Deleting WORK.FITBOTH (memtype=DATA).
NOTE: Deleting WORK.FITF1 (memtype=DATA).
NOTE: Deleting WORK.FITF2 (memtype=DATA).
NOTE: Deleting WORK.LAMBDA (memtype=DATA).
NOTE: Deleting WORK.MODELFITB (memtype=DATA).
NOTE: Deleting WORK.NAMES (memtype=DATA).
NOTE: Deleting WORK.NAMES2 (memtype=DATA).
NOTE: Deleting WORK.NEWNAMES1 (memtype=DATA).
NOTE: Deleting WORK.NEWNAMES2 (memtype=DATA).
NOTE: Deleting WORK.OUT1 (memtype=DATA).
NOTE: Deleting WORK.OUT2 (memtype=DATA).
NOTE: Deleting WORK.OUTCOV (memtype=DATA).
NOTE: Deleting WORK.PARMSF1 (memtype=DATA).
32 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: Deleting WORK.PARMSF2 (memtype=DATA).
NOTE: Deleting WORK.PARMSG1 (memtype=DATA).
NOTE: Deleting WORK.PARMSG2 (memtype=DATA).
NOTE: Deleting WORK.PREDEINTU (memtype=DATA).
NOTE: Deleting WORK.PREDPU (memtype=DATA).
NOTE: Deleting WORK.PREDU1U (memtype=DATA).
NOTE: Deleting WORK.PREDU2U (memtype=DATA).
NOTE: Deleting WORK.PREDX1B1U (memtype=DATA).
NOTE: Deleting WORK.PREDX2B2U (memtype=DATA).
NOTE: Deleting WORK.RANDOM1 (memtype=DATA).
NOTE: Deleting WORK.RANDOM2 (memtype=DATA).
NOTE: Deleting WORK.START1 (memtype=DATA).
NOTE: Deleting WORK.START2 (memtype=DATA).
NOTE: Deleting WORK.START3 (memtype=DATA).
NOTE: Deleting WORK.TRANS1 (memtype=DATA).
NOTE: Deleting WORK.TRANS2 (memtype=DATA).
NOTE: Deleting WORK.ADPRMSF1 (memtype=DATA).
NOTE: Deleting WORK.ADPRMSF2 (memtype=DATA).
NOTE: Deleting WORK.ADPRMSF3 (memtype=DATA).
NOTE: Deleting WORK.START1VARGRP (memtype=DATA).
NOTE: Deleting WORK.START2VARGRP (memtype=DATA).
NOTE: Deleting WORK.AIC (memtype=DATA).
NOTE: Deleting WORK.FINAL (memtype=DATA).
NOTE: Deleting WORK.FINALLL (memtype=DATA).
NOTE: Deleting WORK.FIT3 (memtype=DATA).
NOTE: Deleting WORK.LL (memtype=DATA).
NOTE: Deleting WORK.NAMESF (memtype=DATA).
NOTE: Deleting WORK.NUMPARMS (memtype=DATA).
NOTE: Deleting WORK.RNDM (memtype=DATA).
NOTE: Deleting WORK._PARAM_UNC (memtype=DATA).
NOTE: Deleting WORK.STARTM (memtype=DATA).
NOTE: Deleting WORK._PARAM (memtype=DATA).


NOTE: PROCEDURE DATASETS used (Total process time):
      real time           0.00 seconds
      cpu time            0.01 seconds
      


NOTE: 4 lines were written to file PRINT.
NOTE: The DATA step printed page 8.
NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      

## Execution of MIXTRAN was successful for calibmrec dummywt
## ___________________________________________________________________________________________


NOTE: There were 1 observations read from the data set OUTLIB._PARAM_REDMEAT0.
NOTE: There were 1 observations read from the data set WORK.OUTMIN_AMT.
NOTE: There were 100000 observations read from the data set WORK.SUBJ1REC.
NOTE: The data set WORK.PARAMSUBJ1REC has 100000 observations and 20 variables.
NOTE: DATA statement used (Total process time):
      real time           0.02 seconds
      cpu time            0.03 seconds
      


NOTE: There were 1 observations read from the data set WORK.PARAMSUBJ1REC.
33 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: DATA statement used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      

Note:  Number of 24-hour recall variables is 2
NOTE: Format R24ERRZF has been output.
NOTE: Format R24GTZF has been output.

NOTE: PROCEDURE FORMAT used (Total process time):
      real time           0.01 seconds
      cpu time            0.00 seconds
      


NOTE: There were 100000 observations read from the data set WORK.PARAMSUBJ1REC.
NOTE: The data set WORK._SUBJ1REC has 100000 observations and 25 variables.
NOTE: DATA statement used (Total process time):
      real time           0.03 seconds
      cpu time            0.03 seconds
      


NOTE: There were 100000 observations read from the data set WORK._SUBJ1REC.
NOTE: The PROCEDURE FREQ printed page 9.
NOTE: PROCEDURE FREQ used (Total process time):
      real time           0.01 seconds
      cpu time            0.02 seconds
      

NOTE: IML Ready
NOTE: Module GBC_INVERSE defined.
NOTE: Module BACKTRANSFORM defined.
NOTE: Module MLOGINTN defined.
NOTE: Module MLOGINTD defined.
NOTE: Exiting IML.
NOTE: The data set WORK._RESDATA has 100000 observations and 12 variables.
NOTE: PROCEDURE IML used (Total process time):
      real time           6:06.19
      cpu time            6:06.34
      


NOTE: There were 100000 observations read from the data set WORK._RESDATA.
NOTE: The PROCEDURE FREQ printed page 10.
NOTE: PROCEDURE FREQ used (Total process time):
      real time           0.01 seconds
      cpu time            0.02 seconds
      


NOTE: There were 100000 observations read from the data set WORK._RESDATA.
NOTE: The PROCEDURE FREQ printed page 11.
NOTE: PROCEDURE FREQ used (Total process time):
      real time           0.00 seconds
      cpu time            0.00 seconds
      


NOTE: There were 100000 observations read from the data set WORK.SUBJ1REC.
NOTE: There were 100000 observations read from the data set WORK._RESDATA.
NOTE: The data set WORK.SUBJ1RECRES has 100000 observations and 8 variables.
34 The SAS System                                                                                                   04:16 Friday, September 22, 2017

NOTE: DATA statement used (Total process time):
      real time           0.02 seconds
      cpu time            0.02 seconds
      


NOTE: There were 100000 observations read from the data set WORK.SUBJ1RECRES.
NOTE: The data set WORK.SUBJ1RECRES has 100000 observations and 14 variables.
NOTE: DATA statement used (Total process time):
      real time           0.01 seconds
      cpu time            0.03 seconds
      


NOTE: PROC LOGISTIC is modeling the probability that ybin_event=1.
NOTE: Convergence criterion (GCONV=1E-8) satisfied.
NOTE: There were 100000 observations read from the data set WORK.SUBJ1RECRES.
NOTE: The data set WORK.REGOUT has 1 observations and 10 variables.
NOTE: The PROCEDURE LOGISTIC printed pages 12-13.
NOTE: PROCEDURE LOGISTIC used (Total process time):
      real time           0.14 seconds
      cpu time            0.12 seconds
      


NOTE: There were 1 observations read from the data set WORK.REGOUT.
NOTE: The data set OUTLIB.DIETHEALTHOUT0 has 1 observations and 14 variables.
NOTE: DATA statement used (Total process time):
      real time           0.03 seconds
      cpu time            0.00 seconds
      


NOTE: SAS Institute Inc., SAS Campus Drive, Cary, NC USA 27513-2414
NOTE: The SAS System used:
      real time           7:56.79
      cpu time            7:27.87
      
