Mplus VERSION 6.1 MUTHEN & MUTHEN 10/16/2010 5:09 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 group 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); classes = cg(2) c(2); categorical = group; 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; group = 0; if(tx eq 2)then group = 1; Analysis: type = mixture; starts = 100 40; process = 4(starts); interactive = control.dat; Model: %overall% i s | y0@0 y2@.2 y3@.3 y4@.4 y5@.5 y6@.6 y7@.7 y8@.8 y9@.9; s@0; ! slope variance had negative estimate with 2 classes %cg#1.c#1% ! control group [group$1@15]; [pfs1-pfs9@0]; [i] (mi1); [s] (ms11); %cg#1.c#2% [group$1@15]; ! the latent class covariate c has an effect on pfs: [pfs1*0] (p11); [pfs2*0] (p12); [pfs3*0] (p13); [pfs4*0] (p14); [pfs5*0] (p15); [pfs6*0] (p16); [pfs7*0] (p17); [pfs8*0] (p18); [pfs9*0] (p19); [i] (mi2); [s] (ms12); %cg#2.c#1% ! treatment group [group$1@-15]; [pfs1*0] (p21); [pfs2*0] (p22); [pfs3*0] (p23); [pfs4*0] (p24); [pfs5*0] (p25); [pfs6*0] (p26); [pfs7*0] (p27); [pfs8*0] (p28); [pfs9*0] (p29); ! hold the intercept mean equal across treatment classes ! because treatment has not started at visit 0: [i] (mi1); [s] (ms21); %cg#2.c#2% [group$1@-15]; [pfs1*0] (p31); [pfs2*0] (p32); [pfs3*0] (p33); [pfs4*0] (p34); [pfs5*0] (p35); [pfs6*0] (p36); [pfs7*0] (p37); [pfs8*0] (p38); [pfs9*0] (p39); [i] (mi2); [s] (ms22); Model constraint: new(b1*0 b2*0 b3*0 b3b1*0 b3b2*0 diff9*0 sdif1*0 sdif2*0 pdif1*0 pdif2*0 pdif3*0 pdif4*0 pdif5*0 pdif6*0 pdif7*0 pdif8*0 pdif9*0 ptdif1*0 ptdif2*0 ptdif3*0 ptdif4*0 ptdif5*0 ptdif6*0 ptdif7*0 ptdif8*0 ptdif9*0); b3b1=b3-b1; b3b2=b3-b2; p12=p11+b1*1; p13=p11+b1*2; p14=p11+b1*3; p15=p11+b1*4; p16=p11+b1*5; p17=p11+b1*6; p18=p11+b1*7; p19=p11+b1*8; p22=p21+b2*1; p23=p21+b2*2; p24=p21+b2*3; p25=p21+b2*4; p26=p21+b2*5; p27=p21+b2*6; p28=p21+b2*7; p29=p21+b2*8; p32=p31+b3*1; p33=p31+b3*2; p34=p31+b3*3; p35=p31+b3*4; p36=p31+b3*5; p37=p31+b3*6; p38=p31+b3*7; p39=p31+b3*8; diff9=(p39-p29)-(p19-0); sdif1=ms21-ms11; sdif2=ms22-ms12; pdif1=p31-p11; pdif2=p32-p12; pdif3=p33-p13; pdif4=p34-p14; pdif5=p35-p15; pdif6=p36-p16; pdif7=p37-p17; pdif8=p38-p18; pdif9=p39-p19; ptdif1=p31-p21; ptdif2=p32-p22; ptdif3=p33-p23; ptdif4=p34-p24; ptdif5=p35-p25; ptdif6=p36-p26; ptdif7=p37-p27; ptdif8=p38-p28; ptdif9=p39-p29; Plot: type = plot3; series = y0-y9(s); *** WARNING in MODEL command All continuous latent variable covariances involving S have been fixed to 0 because the variance of S is fixed at 0. 1 WARNING(S) FOUND IN THE INPUT INSTRUCTIONS 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 Number of categorical latent variables 2 Observed dependent variables Continuous Y0 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Binary and ordered categorical (ordinal) GROUP Time-to-event (survival) PFS1 PFS2 PFS3 PFS4 PFS5 PFS6 PFS7 PFS8 PFS9 Continuous latent variables I S Categorical latent variables CG C 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-06 Relative loglikelihood change 0.100D-06 Derivative 0.100D-05 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-05 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-05 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 Random Starts Specifications Number of initial stage random starts 100 Number of final stage optimizations 40 Number of initial stage iterations 10 Initial stage convergence criterion 0.100D+01 Random starts scale 0.500D+01 Random seed for generating random starts 0 Parameterization LOGIT Link LOGIT Base Hazard EQUAL ACROSS CLASSES Input data file(s) all_wide.dat Input data format FREE SUMMARY OF DATA Number of missing data patterns 37 Number of y missing data patterns 37 Number of u missing data patterns 1 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 GROUP Category 1 0.494 120.000 Category 2 0.506 123.000 RANDOM STARTS RESULTS RANKED FROM THE BEST TO THE WORST LOGLIKELIHOOD VALUES Final stage loglikelihood values at local maxima, seeds, and initial stage start numbers: -4598.243 366706 29 -4598.243 85462 51 -4598.243 637345 19 -4598.243 967237 48 -4598.243 481835 57 -4598.243 830392 35 -4598.243 645664 39 -4598.243 902278 21 -4598.243 475420 71 -4598.243 754100 56 -4598.243 260601 36 -4598.243 568859 49 -4598.243 354559 73 -4598.243 575700 100 -4598.243 957392 79 -4598.243 813779 92 -4598.243 311214 64 -4598.243 364676 27 -4598.243 247224 94 -4598.243 120506 45 -4598.243 207896 25 -4598.243 370466 41 -4598.243 696773 80 -4598.432 372176 23 -4598.432 544048 87 -4598.432 569833 85 -4598.432 887676 22 -4598.432 749453 33 -4598.432 391179 78 -4598.432 402224 91 -4608.392 939021 8 -4608.392 903420 5 -4608.392 783110 72 -4608.392 170954 86 -4608.392 608496 4 -4608.392 195873 6 -4608.392 415931 10 -4612.348 471398 74 -4612.981 967902 52 -4614.296 422103 62 WARNING: WHEN ESTIMATING A MODEL WITH MORE THAN TWO CLASSES, IT MAY BE NECESSARY TO INCREASE THE NUMBER OF RANDOM STARTS USING THE STARTS OPTION TO AVOID LOCAL MAXIMA. THE MODEL ESTIMATION TERMINATED NORMALLY MODEL FIT INFORMATION Number of Free Parameters 24 Loglikelihood H0 Value -4598.243 H0 Scaling Correction Factor 1.293 for MLR Information Criteria Akaike (AIC) 9244.486 Bayesian (BIC) 9328.319 Sample-Size Adjusted BIC 9252.243 (n* = (n + 2) / 24) Chi-Square Test of Model Fit for the Binary and Ordered Categorical (Ordinal) Outcomes Pearson Chi-Square Value 0.000 Degrees of freedom cannot be computed for this model part. Likelihood Ratio Chi-Square Value 0.000 Degrees of freedom cannot be computed for this model part. MODEL RESULTS USE THE LATENT CLASS VARIABLE ORDER CG C Latent Class Variable Patterns CG C Class Class 1 1 1 2 2 1 2 2 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASS PATTERNS BASED ON THE ESTIMATED MODEL Latent Class Pattern 1 1 57.30254 0.23581 1 2 62.69746 0.25801 2 1 58.73511 0.24171 2 2 64.26490 0.26446 FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE BASED ON THE ESTIMATED MODEL Latent Class Variable Class CG 1 120.00000 0.49383 2 123.00000 0.50617 C 1 116.03764 0.47752 2 126.96235 0.52248 LATENT TRANSITION PROBABILITIES BASED ON THE ESTIMATED MODEL CG Classes (Rows) by C Classes (Columns) 1 2 1 0.478 0.522 2 0.478 0.522 FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Class Pattern 1 1 55.16451 0.22701 1 2 64.83549 0.26681 2 1 60.87305 0.25051 2 2 62.12695 0.25567 FINAL CLASS COUNTS AND PROPORTIONS FOR EACH LATENT CLASS VARIABLE BASED ON ESTIMATED POSTERIOR PROBABILITIES Latent Class Variable Class CG 1 120.00000 0.49383 2 123.00000 0.50617 C 1 116.03756 0.47752 2 126.96245 0.52248 CLASSIFICATION QUALITY Entropy 0.787 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS PATTERN Class Counts and Proportions Latent Class Pattern 1 1 52 0.21399 1 2 68 0.27984 2 1 58 0.23868 2 2 65 0.26749 Average Latent Class Probabilities for Most Likely Latent Class Pattern (Row) by Latent Class Pattern (Column) Latent Class Variable Patterns Latent Class CG C Pattern No. Class Class 1 1 1 2 1 2 3 2 1 4 2 2 1 2 3 4 1 0.825 0.175 0.000 0.000 2 0.181 0.819 0.000 0.000 3 0.000 0.000 0.942 0.058 4 0.000 0.000 0.096 0.904 CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP FOR EACH LATENT CLASS VARIABLE Latent Class Variable Class CG 1 120 0.49383 2 123 0.50617 C 1 110 0.45267 2 133 0.54733 MODEL RESULTS Two-Tailed Estimate S.E. Est./S.E. P-Value Latent Class Pattern 1 1 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 Means I 34.923 2.388 14.625 0.000 S -20.850 8.269 -2.521 0.012 Means PFS1 0.000 0.000 999.000 999.000 PFS2 0.000 0.000 999.000 999.000 PFS3 0.000 0.000 999.000 999.000 PFS4 0.000 0.000 999.000 999.000 PFS5 0.000 0.000 999.000 999.000 PFS6 0.000 0.000 999.000 999.000 PFS7 0.000 0.000 999.000 999.000 PFS8 0.000 0.000 999.000 999.000 PFS9 0.000 0.000 999.000 999.000 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 Thresholds GROUP$1 15.000 0.000 999.000 999.000 Variances I 186.856 34.251 5.456 0.000 S 0.000 0.000 999.000 999.000 Residual Variances Y0 415.721 63.342 6.563 0.000 Y2 245.399 38.680 6.344 0.000 Y3 252.887 50.484 5.009 0.000 Y4 294.465 78.520 3.750 0.000 Y5 195.000 39.051 4.993 0.000 Y6 118.977 37.225 3.196 0.001 Y7 276.706 59.511 4.650 0.000 Y8 275.174 127.413 2.160 0.031 Y9 293.953 147.830 1.988 0.047 Latent Class Pattern 1 2 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 Means I 57.014 2.850 20.004 0.000 S 21.654 6.830 3.170 0.002 Means PFS1 1.011 0.431 2.348 0.019 PFS2 0.813 0.353 2.305 0.021 PFS3 0.615 0.290 2.121 0.034 PFS4 0.417 0.254 1.639 0.101 PFS5 0.218 0.257 0.849 0.396 PFS6 0.020 0.297 0.067 0.946 PFS7 -0.178 0.363 -0.492 0.623 PFS8 -0.377 0.442 -0.852 0.394 PFS9 -0.575 0.530 -1.085 0.278 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 Thresholds GROUP$1 15.000 0.000 999.000 999.000 Variances I 186.856 34.251 5.456 0.000 S 0.000 0.000 999.000 999.000 Residual Variances Y0 415.721 63.342 6.563 0.000 Y2 245.399 38.680 6.344 0.000 Y3 252.887 50.484 5.009 0.000 Y4 294.465 78.520 3.750 0.000 Y5 195.000 39.051 4.993 0.000 Y6 118.977 37.225 3.196 0.001 Y7 276.706 59.511 4.650 0.000 Y8 275.174 127.413 2.160 0.031 Y9 293.953 147.830 1.988 0.047 Latent Class Pattern 2 1 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 Means I 34.923 2.388 14.625 0.000 S -13.915 3.897 -3.571 0.000 Means PFS1 -2.512 0.624 -4.024 0.000 PFS2 -2.135 0.535 -3.988 0.000 PFS3 -1.758 0.450 -3.909 0.000 PFS4 -1.382 0.370 -3.731 0.000 PFS5 -1.005 0.301 -3.335 0.001 PFS6 -0.628 0.252 -2.494 0.013 PFS7 -0.251 0.234 -1.071 0.284 PFS8 0.126 0.256 0.492 0.623 PFS9 0.503 0.308 1.632 0.103 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 Thresholds GROUP$1 -15.000 0.000 999.000 999.000 Variances I 186.856 34.251 5.456 0.000 S 0.000 0.000 999.000 999.000 Residual Variances Y0 415.721 63.342 6.563 0.000 Y2 245.399 38.680 6.344 0.000 Y3 252.887 50.484 5.009 0.000 Y4 294.465 78.520 3.750 0.000 Y5 195.000 39.051 4.993 0.000 Y6 118.977 37.225 3.196 0.001 Y7 276.706 59.511 4.650 0.000 Y8 275.174 127.413 2.160 0.031 Y9 293.953 147.830 1.988 0.047 Latent Class Pattern 2 2 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 Means I 57.014 2.850 20.004 0.000 S 18.098 8.001 2.262 0.024 Means PFS1 0.046 0.370 0.123 0.902 PFS2 0.248 0.302 0.819 0.413 PFS3 0.450 0.254 1.770 0.077 PFS4 0.652 0.238 2.740 0.006 PFS5 0.854 0.260 3.291 0.001 PFS6 1.056 0.311 3.394 0.001 PFS7 1.259 0.381 3.303 0.001 PFS8 1.461 0.461 3.171 0.002 PFS9 1.663 0.546 3.046 0.002 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 Thresholds GROUP$1 -15.000 0.000 999.000 999.000 Variances I 186.856 34.251 5.456 0.000 S 0.000 0.000 999.000 999.000 Residual Variances Y0 415.721 63.342 6.563 0.000 Y2 245.399 38.680 6.344 0.000 Y3 252.887 50.484 5.009 0.000 Y4 294.465 78.520 3.750 0.000 Y5 195.000 39.051 4.993 0.000 Y6 118.977 37.225 3.196 0.001 Y7 276.706 59.511 4.650 0.000 Y8 275.174 127.413 2.160 0.031 Y9 293.953 147.830 1.988 0.047 Categorical Latent Variables Means CG#1 -0.025 0.128 -0.192 0.847 C#1 -0.090 0.184 -0.488 0.626 New/Additional Parameters B1 -0.198 0.102 -1.940 0.052 B2 0.377 0.097 3.872 0.000 B3 0.202 0.097 2.087 0.037 B3B1 0.400 0.106 3.790 0.000 B3B2 -0.175 0.127 -1.373 0.170 DIFF9 1.735 0.741 2.342 0.019 SDIF1 6.935 8.427 0.823 0.411 SDIF2 -3.556 9.079 -0.392 0.695 PDIF1 -0.966 0.335 -2.881 0.004 PDIF2 -0.565 0.267 -2.120 0.034 PDIF3 -0.165 0.228 -0.722 0.470 PDIF4 0.236 0.236 1.000 0.317 PDIF5 0.636 0.285 2.232 0.026 PDIF6 1.036 0.359 2.884 0.004 PDIF7 1.437 0.447 3.217 0.001 PDIF8 1.837 0.541 3.399 0.001 PDIF9 2.238 0.638 3.507 0.000 PTDIF1 2.558 0.654 3.910 0.000 PTDIF2 2.383 0.546 4.364 0.000 PTDIF3 2.208 0.448 4.926 0.000 PTDIF4 2.034 0.369 5.514 0.000 PTDIF5 1.859 0.322 5.778 0.000 PTDIF6 1.684 0.321 5.240 0.000 PTDIF7 1.510 0.368 4.100 0.000 PTDIF8 1.335 0.447 2.984 0.003 PTDIF9 1.160 0.545 2.129 0.033 RESULTS IN PROBABILITY SCALE Latent Class Pattern 1 1 GROUP Category 1 1.000 0.000 0.000 1.000 Category 2 0.000 0.000 0.000 1.000 Latent Class Pattern 1 2 GROUP Category 1 1.000 0.000 0.000 1.000 Category 2 0.000 0.000 0.000 1.000 Latent Class Pattern 2 1 GROUP Category 1 0.000 0.000 0.000 1.000 Category 2 1.000 0.000 0.000 1.000 Latent Class Pattern 2 2 GROUP Category 1 0.000 0.000 0.000 1.000 Category 2 1.000 0.000 0.000 1.000 LATENT CLASS ODDS RATIO RESULTS Latent Class Pattern 1 1 Compared to Latent Class Pattern 1 2 GROUP Category > 1 1.000 0.000 999.000 999.000 Latent Class Pattern 1 1 Compared to Latent Class Pattern 2 1 GROUP Category > 1 0.000 0.000 999.000 999.000 Latent Class Pattern 1 1 Compared to Latent Class Pattern 2 2 GROUP Category > 1 0.000 0.000 999.000 999.000 Latent Class Pattern 1 2 Compared to Latent Class Pattern 2 1 GROUP Category > 1 0.000 0.000 999.000 999.000 Latent Class Pattern 1 2 Compared to Latent Class Pattern 2 2 GROUP Category > 1 0.000 0.000 999.000 999.000 Latent Class Pattern 2 1 Compared to Latent Class Pattern 2 2 GROUP Category > 1 1.000 0.000 999.000 999.000 QUALITY OF NUMERICAL RESULTS Condition Number for the Information Matrix 0.998E-03 (ratio of smallest to largest eigenvalue) PLOT INFORMATION The following plots are available: Histograms (sample values) Scatterplots (sample values) Survival curves Sample means Estimated means Sample and estimated means Observed individual values Estimated means and observed individual values Mixture distributions Beginning Time: 05:09:17 Ending Time: 05:09:41 Elapsed Time: 00:00:24 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