Mplus VERSION 6.1 MUTHEN & MUTHEN 10/16/2010 5:08 AM INPUT INSTRUCTIONS Data: File is all_wide.dat; Variable: Names are patno age count pritreat tx race gender stage icda mets lnode pmryt survcns surv opfscns opfs pfscns pfs ttopdcns ttopd ttpdcns ttpd tttfcns tttf ttprcns ttpr kps_0 kps_1 kps_2 kps_3 kps_4 kps_5 kps_6 kps_7 kps_8 kps_9 kps_10 kps_11 kps_12 anrx_0 anrx_1 anrx_2 anrx_3 anrx_4 anrx_5 anrx_6 anrx_7 anrx_8 anrx_9 anrx_10 anrx_11 anrx_12 ftg_0 ftg_1 ftg_2 ftg_3 ftg_4 ftg_5 ftg_6 ftg_7 ftg_8 ftg_9 ftg_10 ftg_11 ftg_12 cgh_0 cgh_1 cgh_2 cgh_3 cgh_4 cgh_5 cgh_6 cgh_7 cgh_8 cgh_9 cgh_10 cgh_11 cgh_12 dysp_0 dysp_1 dysp_2 dysp_3 dysp_4 dysp_5 dysp_6 dysp_7 dysp_8 dysp_9 dysp_10 dysp_11 dysp_12 hmpty_0 hmpty_1 hmpty_2 hmpty_3 hmpty_4 hmpty_5 hmpty_6 hmpty_7 hmpty_8 hmpty_9 hmpty_10 hmpty_11 hmpty_12 pain_0 pain_1 pain_2 pain_3 pain_4 pain_5 pain_6 pain_7 pain_8 pain_9 pain_10 pain_11 pain_12 sx_0 sx_1 sx_2 sx_3 sx_4 sx_5 sx_6 sx_7 sx_8 sx_9 sx_10 sx_11 sx_12 intfr_0 intfr_1 intfr_2 intfr_3 intfr_4 intfr_5 intfr_6 intfr_7 intfr_8 intfr_9 intfr_10 intfr_11 intfr_12 !qol_0 qol_1 qol_2 qol_3 qol_4 qol_5 qol_6 qol_7 qol_8 qol_9 qol_10 y0-y10 qol_11 qol_12; Missing are all (-9999) ; Usev = y0 y2-y9 tx pfs1-pfs9 c1-c9; survival = pfs1(all) pfs2(all) pfs3(all) pfs4(all) pfs5(all) pfs6(all) pfs7(all) pfs8(all) pfs9(all); timecensored = c1 c2 c3 c4 c5 c6 c7 c8 c9 (0 = not 1 = right); categorical=tx; Define: if (pfs>=0.7) then pfs1=0.7; if (pfs>=0.7) then c1=1; if (pfs<0.7) then pfs1=pfs; if (pfs<0.7) then c1=pfscns; if (pfs>=1.4) then pfs2=0.7; if (pfs>=1.4) then c2=1; if (pfs<1.4) then pfs2=pfs-0.7; if (pfs<1.4) then c2=pfscns; if (pfs<0.7) then pfs2=_missing; if (pfs<0.7) then c2=_missing; if (pfs>=2.1) then pfs3=0.7; if (pfs>=2.1) then c3=1; if (pfs<2.1) then pfs3=pfs-1.4; if (pfs<2.1) then c3=pfscns; if (pfs<1.4) then pfs3=_missing; if (pfs<1.4) then c3=_missing; if (pfs>=2.8) then pfs4=0.7; if (pfs>=2.8) then c4=1; if (pfs<2.8) then pfs4=pfs-2.1; if (pfs<2.8) then c4=pfscns; if (pfs<2.1) then pfs4=_missing; if (pfs<2.1) then c4=_missing; if (pfs>=3.5) then pfs5=0.7; if (pfs>=3.5) then c5=1; if (pfs<3.5) then pfs5=pfs-2.8; if (pfs<3.5) then c5=pfscns; if (pfs<2.8) then pfs5=_missing; if (pfs<2.8) then c5=_missing; if (pfs>=4.2) then pfs6=0.7; if (pfs>=4.2) then c6=1; if (pfs<4.2) then pfs6=pfs-3.5; if (pfs<4.2) then c6=pfscns; if (pfs<3.5) then pfs6=_missing; if (pfs<3.5) then c6=_missing; if (pfs>=4.9) then pfs7=0.7; if (pfs>=4.9) then c7=1; if (pfs<4.9) then pfs7=pfs-4.2; if (pfs<4.9) then c7=pfscns; if (pfs<4.2) then pfs7=_missing; if (pfs<4.2) then c7=_missing; if (pfs>=5.6) then pfs8=0.7; if (pfs>=5.6) then c8=1; if (pfs<5.6) then pfs8=pfs-4.9; if (pfs<5.6) then c8=pfscns; if (pfs<4.9) then pfs8=_missing; if (pfs<4.9) then c8=_missing; pfs9=pfs-5.6; c9=pfscns; if (pfs<5.6) then pfs9=_missing; if (pfs<5.6) then c9=_missing; Analysis: algo=int; integration=monte; process = 4(starts); Model: i s | y0@0 y2@.2 y3@.3 y4@.4 y5@.5 y6@.6 y7@.7 y8@.8 y9@.9; s on tx; s with i; pfs1 on tx (p1); pfs2 on tx (p2); pfs3 on tx (p3); pfs4 on tx (p4); pfs5 on tx (p5); pfs6 on tx (p6); pfs7 on tx (p7); pfs8 on tx (p8); pfs9 on tx (p9); ! effect of y on pfs held equal across time pfs1 on y0 (1); pfs2 on y2 (1); pfs3 on y3 (1); pfs4 on y4 (1); pfs5 on y5 (1); pfs6 on y6 (1); pfs7 on y7 (1); pfs8 on y8 (1); pfs9 on y9 (1); Model constraint: new(b*0); p2 = p1+b*1; p3 = p1+b*2; p4 = p1+b*3; p5 = p1+b*4; p6 = p1+b*5; p7 = p1+b*6; p8 = p1+b*7; p9 = p1+b*8; Plot: type = plot3; series = y0-y9(s); INPUT READING TERMINATED NORMALLY SUMMARY OF ANALYSIS Number of groups 1 Number of observations 243 Number of dependent variables 19 Number of independent variables 0 Number of continuous latent variables 2 Observed dependent variables Continuous Y0 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Binary and ordered categorical (ordinal) TX Time-to-event (survival) PFS1 PFS2 PFS3 PFS4 PFS5 PFS6 PFS7 PFS8 PFS9 Continuous latent variables I S Variables with special functions Time-censoring variables C1 C2 C3 C4 C5 C6 C7 C8 C9 Estimator MLR Information matrix OBSERVED Optimization Specifications for the Quasi-Newton Algorithm for Continuous Outcomes Maximum number of iterations 100 Convergence criterion 0.100D-05 Optimization Specifications for the EM Algorithm Maximum number of iterations 500 Convergence criteria Loglikelihood change 0.100D-02 Relative loglikelihood change 0.100D-05 Derivative 0.100D-02 Optimization Specifications for the M step of the EM Algorithm for Categorical Latent variables Number of M step iterations 1 M step convergence criterion 0.100D-02 Basis for M step termination ITERATION Optimization Specifications for the M step of the EM Algorithm for Censored, Binary or Ordered Categorical (Ordinal), Unordered Categorical (Nominal) and Count Outcomes Number of M step iterations 1 M step convergence criterion 0.100D-02 Basis for M step termination ITERATION Maximum value for logit thresholds 15 Minimum value for logit thresholds -15 Minimum expected cell size for chi-square 0.100D-01 Maximum number of iterations for H1 2000 Convergence criterion for H1 0.100D-03 Optimization algorithm EMA Integration Specifications Type MONTECARLO Number of integration points 225 Dimensions of numerical integration 9 Adaptive quadrature ON Monte Carlo integration seed 0 Link LOGIT Base Hazard OFF Cholesky OFF Input data file(s) all_wide.dat Input data format FREE SUMMARY OF DATA Number of missing data patterns 37 COVARIANCE COVERAGE OF DATA Minimum covariance coverage value 0.100 PROPORTION OF DATA PRESENT FOR Y Covariance Coverage Y0 Y2 Y3 Y4 Y5 ________ ________ ________ ________ ________ Y0 0.881 Y2 0.630 0.683 Y3 0.519 0.531 0.564 Y4 0.391 0.407 0.374 0.420 Y5 0.337 0.342 0.325 0.325 0.358 Y6 0.276 0.276 0.272 0.259 0.276 Y7 0.230 0.230 0.222 0.214 0.226 Y8 0.169 0.169 0.156 0.169 0.169 Y9 0.123 0.119 0.119 0.119 0.123 Covariance Coverage Y6 Y7 Y8 Y9 ________ ________ ________ ________ Y6 0.284 Y7 0.218 0.235 Y8 0.152 0.148 0.177 Y9 0.115 0.115 0.119 0.123 UNIVARIATE PROPORTIONS AND COUNTS FOR CATEGORICAL VARIABLES TX Category 1 0.494 120.000 Category 2 0.506 123.000 THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 19 Loglikelihood H0 Value -4609.818 H0 Scaling Correction Factor 1.322 for MLR Information Criteria Akaike (AIC) 9257.636 Bayesian (BIC) 9324.004 Sample-Size Adjusted BIC 9263.777 (n* = (n + 2) / 24) MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value I | Y0 1.000 0.000 999.000 999.000 Y2 1.000 0.000 999.000 999.000 Y3 1.000 0.000 999.000 999.000 Y4 1.000 0.000 999.000 999.000 Y5 1.000 0.000 999.000 999.000 Y6 1.000 0.000 999.000 999.000 Y7 1.000 0.000 999.000 999.000 Y8 1.000 0.000 999.000 999.000 Y9 1.000 0.000 999.000 999.000 S | Y0 0.000 0.000 999.000 999.000 Y2 0.200 0.000 999.000 999.000 Y3 0.300 0.000 999.000 999.000 Y4 0.400 0.000 999.000 999.000 Y5 0.500 0.000 999.000 999.000 Y6 0.600 0.000 999.000 999.000 Y7 0.700 0.000 999.000 999.000 Y8 0.800 0.000 999.000 999.000 Y9 0.900 0.000 999.000 999.000 S ON TX -8.810 6.941 -1.269 0.204 PFS1 ON TX -1.249 0.230 -5.422 0.000 Y0 0.014 0.003 4.555 0.000 PFS2 ON TX -0.963 0.193 -4.983 0.000 Y2 0.014 0.003 4.555 0.000 PFS3 ON TX -0.678 0.165 -4.107 0.000 Y3 0.014 0.003 4.555 0.000 PFS4 ON TX -0.392 0.150 -2.608 0.009 Y4 0.014 0.003 4.555 0.000 PFS5 ON TX -0.107 0.153 -0.696 0.486 Y5 0.014 0.003 4.555 0.000 PFS6 ON TX 0.179 0.173 1.033 0.302 Y6 0.014 0.003 4.555 0.000 PFS7 ON TX 0.464 0.205 2.268 0.023 Y7 0.014 0.003 4.555 0.000 PFS8 ON TX 0.750 0.244 3.077 0.002 Y8 0.014 0.003 4.555 0.000 PFS9 ON TX 1.035 0.287 3.607 0.000 Y9 0.014 0.003 4.555 0.000 S WITH I 42.937 78.117 0.550 0.583 Means I 46.733 1.631 28.650 0.000 Intercepts Y0 0.000 0.000 999.000 999.000 Y2 0.000 0.000 999.000 999.000 Y3 0.000 0.000 999.000 999.000 Y4 0.000 0.000 999.000 999.000 Y5 0.000 0.000 999.000 999.000 Y6 0.000 0.000 999.000 999.000 Y7 0.000 0.000 999.000 999.000 Y8 0.000 0.000 999.000 999.000 Y9 0.000 0.000 999.000 999.000 S 2.225 6.317 0.352 0.725 Thresholds TX$1 -0.025 0.128 -0.192 0.847 Variances I 369.961 55.034 6.722 0.000 Residual Variances Y0 372.148 70.901 5.249 0.000 Y2 222.078 35.664 6.227 0.000 Y3 278.262 59.048 4.713 0.000 Y4 326.238 79.187 4.120 0.000 Y5 186.780 39.947 4.676 0.000 Y6 128.798 33.962 3.792 0.000 Y7 239.229 54.818 4.364 0.000 Y8 308.419 157.633 1.957 0.050 Y9 340.029 115.663 2.940 0.003 S 402.622 163.038 2.470 0.014 New/Additional Parameters B 0.285 0.053 5.433 0.000 RESULTS IN PROBABILITY SCALE TX Category 1 0.494 0.032 15.397 0.000 Category 2 0.506 0.032 15.782 0.000 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.155E-06 (ratio of smallest to largest eigenvalue) SAMPLE STATISTICS FOR ESTIMATED FACTOR SCORES SAMPLE STATISTICS Means I S ________ ________ 1 46.501 -2.582 Covariances I S ________ ________ I 249.689 S 81.754 104.865 Correlations I S ________ ________ I 1.000 S 0.505 1.000 PLOT INFORMATION The following plots are available: Histograms (sample values, estimated factor scores) Scatterplots (sample values, estimated factor scores) Survival curves Sample means Estimated means Sample and estimated means Observed individual values Beginning Time: 05:08:29 Ending Time: 05:08:47 Elapsed Time: 00:00:18 MUTHEN & MUTHEN 3463 Stoner Ave. Los Angeles, CA 90066 Tel: (310) 391-9971 Fax: (310) 391-8971 Web: www.StatModel.com Support: Support@StatModel.com Copyright (c) 1998-2010 Muthen & Muthen