Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017   1
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome

The SURVEYSELECT Procedure

Selection Method    Unrestricted Random Sampling

Input Data Set                      CALIB_SUBSTUDY
Random Number Seed                           14143
Sample Size                                   1000
Expected Number of Hits                          1
Sampling Weight                                  1
Number of Replicates                           200
Total Sample Size                           200000
Output Data Set            BOOTDATA_CALIB_SUBSTUDY
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017   2
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome

The SURVEYSELECT Procedure

Selection Method    Unrestricted Random Sampling

Input Data Set                      MAIN_STUDY
Random Number Seed                       63491
Sample Size                              99000
Expected Number of Hits                      1
Sampling Weight                              1
Number of Replicates                       200
Total Sample Size                     19800000
Output Data Set            BOOTDATA_MAIN_STUDY
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017   3
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Subjects Grouped Into Number of Observed 24-Hour Recalls by The Number of Recalls With Positive Value
Percents Are Weighted If A Weight Is In Use. Counts Are Not Weighted

             Number of
           Recalls with
             response
 Number    greater than                 Percent of
   of         0 (i.e.      Frequency       Total
Recalls       eaten)         Count       Frequency

   2             0            140          14.0   
   2             1            299          29.9   
   2             2            561          56.1   
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument              04:16 Friday, September 22, 2017   4
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Results from Fitting Uncorrelated (amount or nocorr) Model
Response Variable: r_redmeat_g

Convergence Status:
   Probability Model -- NOTE: GCONV convergence criterion satisfied.
   Amount Model -- NOTE: GCONV convergence criterion satisfied.


________________________________________________________________________________
 Parameter                Name                     Estimate    Std Err Prob>|t|
________________________________________________________________________________
 P01_INTERCEPT            Intercept--pr              1.2593    0.0895  0.0000
 P02_STD_BOXCOX_FFQ_REDMEAstd_boxcox_ffq_redmeat_g-  0.8845    0.0874  0.0000
 P03_STD_ENTRY_AGE        std_entry_age--pr          0.0112    0.0700  0.8724
 P04_STD_LOG_BMI          std_log_bmi--pr            0.0410    0.0717  0.5675
 P_LOGSDU1                Reparam Var(u1)--pr        0.2000    0.1179  0.0900
 A01_INTERCEPT            Intercept--am             10.9256    0.6950  0.0000
 A02_STD_BOXCOX_FFQ_REDMEAstd_boxcox_ffq_redmeat_g-  0.9538    0.1590  0.0000
 A03_STD_ENTRY_AGE        std_entry_age--am         -0.3375    0.1236  0.0064
 A04_STD_LOG_BMI          std_log_bmi--am            0.2444    0.1221  0.0457
 A_LAMBDA                 lambda--am                 0.3485    0.0207  0.0000
 A_LOGSDE                 Resid, Reparam--am         1.3614    0.0952  0.0000
 A_LOGSDU2                Reparam Var(u2)--am        0.6007    0.1475  0.0001
                                                                    .       .
                                                                    .       .
                                                                    .       .
                                                                    .       .
                                                                    .       .
________________________________________________________________________________
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument              04:16 Friday, September 22, 2017   5
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Results from Fitting Uncorrelated (amount or nocorr) Model
Response Variable: r_redmeat_g


  ______________________________________________________________________
    Name                                Value           Sum
  ______________________________________________________________________
    AIC--bi                             2204.62               .
    AIC--amount                        16262.85        18467.47
    -2 Log Likelihood--bi               2194.62               .
    -2 Log Likelihood--amount          16248.85        18443.47
  ______________________________________________________________________
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument              04:16 Friday, September 22, 2017   6
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Results from Fitting Correlated Model
Response Variable: r_redmeat_g

Convergence Status:
   NOTE: GCONV convergence criterion satisfied.


________________________________________________________________________________
 Parameter                Name                     Estimate    Std Err Prob>|t|
________________________________________________________________________________
 P01_INTERCEPT            Intercept--pr              1.2574    0.0894  0.0000
 P02_STD_BOXCOX_FFQ_REDMEAstd_boxcox_ffq_redmeat_g-  0.8777    0.0869  0.0000
 P03_STD_ENTRY_AGE        std_entry_age--pr          0.0058    0.0701  0.9341
 P04_STD_LOG_BMI          std_log_bmi--pr            0.0403    0.0716  0.5734
 P_LOGSDU1                Reparam Var(u1)--pr        0.2019    0.1174  0.0858
 A01_INTERCEPT            Intercept--am             10.8901    0.6976  0.0000
 A02_STD_BOXCOX_FFQ_REDMEAstd_boxcox_ffq_redmeat_g-  1.0403    0.1712  0.0000
 A03_STD_ENTRY_AGE        std_entry_age--am         -0.3275    0.1252  0.0091
 A04_STD_LOG_BMI          std_log_bmi--am            0.2393    0.1239  0.0538
 A_LAMBDA                 lambda--am                 0.3516    0.0208  0.0000
 A_LOGSDE                 Resid, Reparam--am         1.3728    0.0956  0.0000
 A_LOGSDU2                Reparam Var(u2)--am        0.6341    0.1440  0.0000
 Z_U                      Z-trans of Correlation     0.2893    0.2077  0.1639
________________________________________________________________________________
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument              04:16 Friday, September 22, 2017   7
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Results from Fitting Correlated Model
Response Variable: r_redmeat_g


  ______________________________________________________________________
    Name                Value     Diff in -2ll   p-value
  ______________________________________________________________________
    AIC                 18467.73         .            .
    -2 Log Likelihood   18441.73      1.74       0.1872
  ______________________________________________________________________
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017   8
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
********************************************************************************
 End of MIXTRAN Macro Call for redmeat0 CORR                                 **
 Execution of MIXTRAN was successful ;
********************************************************************************
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017   9
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Frequency of 24-Hour Recall Variables

The FREQ Procedure

r1_redmeat_                             Cumulative    Cumulative
          g    Frequency     Percent     Frequency      Percent
----------------------------------------------------------------
          .      100000      100.00        100000       100.00  


r2_redmeat_                             Cumulative    Cumulative
          g    Frequency     Percent     Frequency      Percent
----------------------------------------------------------------
          .      100000      100.00        100000       100.00  
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017  10
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Number of Observed 24-Hour Recalls and Number of 24-Hour Recalls Greater Than 0

The FREQ Procedure

Table of nirep by totcons

nirep     totcons

Frequency|       0|  Total
---------+--------+
       0 | 100000 | 100000
---------+--------+
Total      100000   100000
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017  11
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Return Codes from Optimization Step of Adaptive Gaussian Quadrature.  Positive Value Indicates Successful Termination.

The FREQ Procedure

                                Cumulative    Cumulative
rcn    Frequency     Percent     Frequency      Percent
--------------------------------------------------------
  3       79141       79.14         79141        79.14  
  6       20859       20.86        100000       100.00  


                                Cumulative    Cumulative
rcd    Frequency     Percent     Frequency      Percent
--------------------------------------------------------
  3      100000      100.00        100000       100.00  
Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017  12
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Logistic Regression Risk Model: Adjusted for Measurement Error

The LOGISTIC Procedure

              Model Information

Data Set                      WORK.SUBJ1RECRES
Response Variable             ybin_event      
Number of Response Levels     2               
Model                         binary logit    
Optimization Technique        Fisher's scoring


Number of Observations Read      100000
Number of Observations Used      100000


          Response Profile
 
 Ordered     ybin_            Total
   Value     event        Frequency

       1            0         85691
       2            1         14309

Probability modeled is ybin_event=1.


                    Model Convergence Status

         Convergence criterion (GCONV=1E-8) satisfied.          


         Model Fit Statistics
 
                             Intercept
              Intercept            and
Criterion          Only     Covariates

AIC           82108.664      75116.921
SC            82118.177      75154.973
-2 Log L      82106.664      75108.921


        Testing Global Null Hypothesis: BETA=0
 
Test                 Chi-Square       DF     Pr > ChiSq

Likelihood Ratio      6997.7432        3         <.0001
Score                 7037.4020        3         <.0001
Wald                  6336.6036        3         <.0001


Fit Univariate Measurement Error Model Using MLE with FFQ as Main Instrument                                    04:16 Friday, September 22, 2017  13
Predict Intake and Perform Regression Calibration
Assess Relationship between a Dietary Component and a Health Outcome
Replicate 0
Logistic Regression Risk Model: Adjusted for Measurement Error

The LOGISTIC Procedure

                   Analysis of Maximum Likelihood Estimates
 
                                          Standard          Wald
Parameter               DF    Estimate       Error    Chi-Square    Pr > ChiSq

Intercept                1     -3.7454      0.0333    12680.5157        <.0001
predint_redmeat_unit     1      1.4075      0.0225     3919.8544        <.0001
std_entry_age_unit_i     1      0.1790      0.0168      113.7599        <.0001
std_log_bmi_unit_iqr     1      0.4527      0.0118     1461.5208        <.0001


                  Odds Ratio Estimates
                            
                           Point          95% Wald
Effect                  Estimate      Confidence Limits

predint_redmeat_unit       4.086       3.910       4.270
std_entry_age_unit_i       1.196       1.157       1.236
std_log_bmi_unit_iqr       1.573       1.536       1.609


Association of Predicted Probabilities and Observed Responses

Percent Concordant          70.5    Somers' D    0.416
Percent Discordant          28.9    Gamma        0.419
Percent Tied                 0.6    Tau-a        0.102
Pairs                 1226152519    c            0.708
