Pattern Mixture approach to analysis of imps79 1 The Mixed Procedure Model Information Data Set WORK.FOUR Dependent Variable imps79 Covariance Structure Unstructured Subject Effect id Estimation Method ML Residual Variance Method Profile Fixed Effects SE Method Kenward-Roger Degrees of Freedom Method Kenward-Roger Class Level Information Class Levels Values id 437 not printed Dimensions Covariance Parameters 4 Columns in X 4 Columns in Z Per Subject 2 Subjects 437 Max Obs Per Subject 5 Number of Observations Number of Observations Read 1603 Number of Observations Used 1603 Number of Observations Not Used 0 Iteration History Iteration Evaluations -2 Log Like Criterion 0 1 5191.43394785 1 2 4649.49942319 0.00054622 2 1 4649.00415990 0.00000607 3 1 4648.99895119 0.00000000 Convergence criteria met. Pattern Mixture approach to analysis of imps79 2 The Mixed Procedure Estimated G Matrix Row Effect id Col1 Col2 1 Intercept 1103 0.3687 0.02085 2 sweek 1103 0.02085 0.2420 Estimated G Correlation Matrix Row Effect id Col1 Col2 1 Intercept 1103 1.0000 0.06980 2 sweek 1103 0.06980 1.0000 Covariance Parameter Estimates Standard Z Cov Parm Subject Estimate Error Value Pr Z UN(1,1) id 0.3687 0.06035 6.11 <.0001 UN(2,1) id 0.02085 0.03376 0.62 0.5368 UN(2,2) id 0.2420 0.03426 7.06 <.0001 Residual 0.5778 0.03049 18.95 <.0001 Fit Statistics -2 Log Likelihood 4649.0 AIC (smaller is better) 4665.0 AICC (smaller is better) 4665.1 BIC (smaller is better) 4697.6 Null Model Likelihood Ratio Test DF Chi-Square Pr > ChiSq 3 542.43 <.0001 Solution for Fixed Effects Standard Effect Estimate Error DF t Value Pr > |t| Intercept 5.3480 0.08791 445 60.84 <.0001 sweek -0.3361 0.06798 414 -4.94 <.0001 drug 0.04634 0.1011 442 0.46 0.6471 sweek*drug -0.6405 0.07755 404 -8.26 <.0001 Pattern Mixture approach to analysis of imps79 3 The Mixed Procedure Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F sweek 1 414 24.45 <.0001 drug 1 442 0.21 0.6471 sweek*drug 1 404 68.21 <.0001 Pattern Mixture approach to analysis of imps79 4 The FREQ Procedure Table of drug by dropout drug dropout Frequency‚ Percent ‚ Row Pct ‚ Col Pct ‚ 0‚ 1‚ Total ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 0 ‚ 70 ‚ 38 ‚ 108 ‚ 16.02 ‚ 8.70 ‚ 24.71 ‚ 64.81 ‚ 35.19 ‚ ‚ 20.90 ‚ 37.25 ‚ ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ 1 ‚ 265 ‚ 64 ‚ 329 ‚ 60.64 ‚ 14.65 ‚ 75.29 ‚ 80.55 ‚ 19.45 ‚ ‚ 79.10 ‚ 62.75 ‚ ƒƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆƒƒƒƒƒƒƒƒˆ Total 335 102 437 76.66 23.34 100.00 Pattern Mixture approach to analysis of imps79 5 The Mixed Procedure Model Information Data Set WORK.FOUR Dependent Variable imps79 Covariance Structure Unstructured Subject Effect id Estimation Method ML Residual Variance Method Profile Fixed Effects SE Method Kenward-Roger Degrees of Freedom Method Kenward-Roger Class Level Information Class Levels Values id 437 not printed Dimensions Covariance Parameters 4 Columns in X 8 Columns in Z Per Subject 2 Subjects 437 Max Obs Per Subject 5 Number of Observations Number of Observations Read 1603 Number of Observations Used 1603 Number of Observations Not Used 0 Iteration History Iteration Evaluations -2 Log Like Criterion 0 1 5146.77162826 1 2 4623.41794615 0.00016103 2 1 4623.27791796 0.00000054 3 1 4623.27746125 0.00000000 Convergence criteria met. Pattern Mixture approach to analysis of imps79 6 The Mixed Procedure Estimated G Matrix Row Effect id Col1 Col2 1 Intercept 1103 0.3612 0.01175 2 sweek 1103 0.01175 0.2300 Estimated G Correlation Matrix Row Effect id Col1 Col2 1 Intercept 1103 1.0000 0.04076 2 sweek 1103 0.04076 1.0000 Covariance Parameter Estimates Standard Z Cov Parm Subject Estimate Error Value Pr Z UN(1,1) id 0.3612 0.05981 6.04 <.0001 UN(2,1) id 0.01175 0.03336 0.35 0.7247 UN(2,2) id 0.2300 0.03300 6.97 <.0001 Residual 0.5768 0.03032 19.02 <.0001 Fit Statistics -2 Log Likelihood 4623.3 AIC (smaller is better) 4647.3 AICC (smaller is better) 4647.5 BIC (smaller is better) 4696.2 Null Model Likelihood Ratio Test DF Chi-Square Pr > ChiSq 3 523.49 <.0001 Solution for Fixed Effects Standard Effect Estimate Error DF t Value Pr > |t| Intercept 5.2210 0.1075 427 48.55 <.0001 sweek -0.3934 0.07635 349 -5.15 <.0001 drug 0.2017 0.1208 426 1.67 0.0958 sweek*drug -0.5386 0.08581 348 -6.28 <.0001 Pattern Mixture approach to analysis of imps79 7 The Mixed Procedure Solution for Fixed Effects Standard Effect Estimate Error DF t Value Pr > |t| dropout 0.3203 0.1864 479 1.72 0.0864 sweek*dropout 0.2517 0.1594 672 1.58 0.1148 drug*dropout -0.3987 0.2270 482 -1.76 0.0796 sweek*drug*dropout -0.6348 0.1961 686 -3.24 0.0013 Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F sweek 1 349 26.55 <.0001 drug 1 426 2.79 0.0958 sweek*drug 1 348 39.40 <.0001 dropout 1 479 2.95 0.0864 sweek*dropout 1 672 2.49 0.1148 drug*dropout 1 482 3.09 0.0796 sweek*drug*dropout 1 686 10.47 0.0013 Estimates Standard Label Estimate Error DF t Value Pr > |t| avg int 5.2958 0.08977 438 58.99 <.0001 avg sweek -0.3346 0.06702 424 -4.99 <.0001 avg drug 0.1086 0.1029 440 1.06 0.2918 avg sweek*drug -0.6868 0.07760 437 -8.85 <.0001 Pattern Mixture approach to analysis of imps79 8 The Mixed Procedure Model Information Data Set WORK.FOUR Dependent Variable imps79 Covariance Structure Unstructured Subject Effect id Estimation Method ML Residual Variance Method Profile Fixed Effects SE Method Kenward-Roger Degrees of Freedom Method Kenward-Roger Class Level Information Class Levels Values id 437 not printed Dimensions Covariance Parameters 4 Columns in X 8 Columns in Z Per Subject 2 Subjects 437 Max Obs Per Subject 5 Number of Observations Number of Observations Read 1603 Number of Observations Used 1603 Number of Observations Not Used 0 Iteration History Iteration Evaluations -2 Log Like Criterion 0 1 5146.77162826 1 2 4623.41794615 0.00016103 2 1 4623.27791796 0.00000054 3 1 4623.27746125 0.00000000 Convergence criteria met. Pattern Mixture approach to analysis of imps79 9 The Mixed Procedure Estimated G Matrix Row Effect id Col1 Col2 1 Intercept 1103 0.3612 0.01175 2 sweek 1103 0.01175 0.2300 Estimated G Correlation Matrix Row Effect id Col1 Col2 1 Intercept 1103 1.0000 0.04076 2 sweek 1103 0.04076 1.0000 Covariance Parameter Estimates Standard Z Cov Parm Subject Estimate Error Value Pr Z UN(1,1) id 0.3612 0.05981 6.04 <.0001 UN(2,1) id 0.01175 0.03336 0.35 0.7247 UN(2,2) id 0.2300 0.03300 6.97 <.0001 Residual 0.5768 0.03032 19.02 <.0001 Fit Statistics -2 Log Likelihood 4623.3 AIC (smaller is better) 4647.3 AICC (smaller is better) 4647.5 BIC (smaller is better) 4696.2 Null Model Likelihood Ratio Test DF Chi-Square Pr > ChiSq 3 523.49 <.0001 Solution for Fixed Effects Standard Effect Estimate Error DF t Value Pr > |t| Intercept 5.2210 0.1075 427 48.55 <.0001 sweek -0.3934 0.07635 349 -5.15 <.0001 drug 0.2017 0.1208 426 1.67 0.0958 sweek*drug -0.5386 0.08581 348 -6.28 <.0001 Pattern Mixture approach to analysis of imps79 10 The Mixed Procedure Solution for Fixed Effects Standard Effect Estimate Error DF t Value Pr > |t| dropout 0.3203 0.1864 479 1.72 0.0864 sweek*dropout 0.2517 0.1594 672 1.58 0.1148 drug*dropout -0.3987 0.2270 482 -1.76 0.0796 sweek*drug*dropout -0.6348 0.1961 686 -3.24 0.0013 Type 3 Tests of Fixed Effects Num Den Effect DF DF F Value Pr > F sweek 1 349 26.55 <.0001 drug 1 426 2.79 0.0958 sweek*drug 1 348 39.40 <.0001 dropout 1 479 2.95 0.0864 sweek*dropout 1 672 2.49 0.1148 drug*dropout 1 482 3.09 0.0796 sweek*drug*dropout 1 686 10.47 0.0013 Estimates Standard Label Estimate Error DF t Value Pr > |t| avg int 5.3337 0.08792 455 60.67 <.0001 avg sweek -0.3048 0.06981 530 -4.37 <.0001 avg drug 0.1241 0.1043 436 1.19 0.2345 avg sweek*drug -0.6621 0.07716 408 -8.58 <.0001