Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 10:24 PM
INPUT INSTRUCTIONS
title:
this is an example of continuous-time survival
analysis using a Cox regression model to
estimate a treatment effect
montecarlo:
names are t u x;
categorical=u;
survival=t;
generate=t(s) u(1);
hazardc = t (0.7);
nobs=500;
class=c(2);
genclass=c(2);
nrep=1;
save=ex7.30.dat;
model montecarlo:
%overall%
[t#1*1]; x*1;
t on x*0.5;
%c#1%
[u$1@15]; ! control group, u=0 (with Prob=1)
[t*0];
%c#2%
[u$1@-15]; ! tx group, u=1 (with Prob=1)
[t*1];
analysis:
type=mixture;
model:
%overall%
t on x*0.5;
%c#1%
[u$1@15];
[t@0];
%c#2%
[u$1@-15];
[t*1];
output: tech9;
INPUT READING TERMINATED NORMALLY
this is an example of continuous-time survival
analysis using a Cox regression model to
estimate a treatment effect
SUMMARY OF ANALYSIS
Number of groups 1
Number of observations 500
Number of replications
Requested 1
Completed 1
Value of seed 0
Number of dependent variables 2
Number of independent variables 1
Number of continuous latent variables 0
Number of categorical latent variables 1
Observed dependent variables
Binary and ordered categorical (ordinal)
U
Time-to-event (survival)
Non-parametric
T
Observed independent variables
X
Categorical latent variables
C
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
Optimization algorithm EMA
Link LOGIT
Base Hazard EQUAL ACROSS CLASSES
SAMPLE STATISTICS FOR THE FIRST REPLICATION
SAMPLE STATISTICS
Means
X
________
0.030
Covariances
X
________
X 1.121
Correlations
X
________
X 1.000
MODEL FIT INFORMATION
Number of Free Parameters 3
Loglikelihood
H0 Value
Mean -98.475
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 -98.475 -98.475
0.980 0.000 -98.475 -98.475
0.950 0.000 -98.475 -98.475
0.900 0.000 -98.475 -98.475
0.800 0.000 -98.475 -98.475
0.700 0.000 -98.475 -98.475
0.500 0.000 -98.475 -98.475
0.300 0.000 -98.475 -98.475
0.200 0.000 -98.475 -98.475
0.100 0.000 -98.475 -98.475
0.050 0.000 -98.475 -98.475
0.020 0.000 -98.475 -98.475
0.010 0.000 -98.475 -98.475
Information Criteria
Akaike (AIC)
Mean 202.951
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 202.951 202.951
0.980 0.000 202.951 202.951
0.950 0.000 202.951 202.951
0.900 0.000 202.951 202.951
0.800 0.000 202.951 202.951
0.700 0.000 202.951 202.951
0.500 0.000 202.951 202.951
0.300 0.000 202.951 202.951
0.200 0.000 202.951 202.951
0.100 0.000 202.951 202.951
0.050 0.000 202.951 202.951
0.020 0.000 202.951 202.951
0.010 0.000 202.951 202.951
Bayesian (BIC)
Mean 215.595
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 215.595 215.595
0.980 0.000 215.595 215.595
0.950 0.000 215.595 215.595
0.900 0.000 215.595 215.595
0.800 0.000 215.595 215.595
0.700 0.000 215.595 215.595
0.500 0.000 215.595 215.595
0.300 0.000 215.595 215.595
0.200 0.000 215.595 215.595
0.100 0.000 215.595 215.595
0.050 0.000 215.595 215.595
0.020 0.000 215.595 215.595
0.010 0.000 215.595 215.595
Sample-Size Adjusted BIC (n* = (n + 2) / 24)
Mean 206.072
Std Dev 0.000
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 206.072 206.072
0.980 0.000 206.072 206.072
0.950 0.000 206.072 206.072
0.900 0.000 206.072 206.072
0.800 0.000 206.072 206.072
0.700 0.000 206.072 206.072
0.500 0.000 206.072 206.072
0.300 0.000 206.072 206.072
0.200 0.000 206.072 206.072
0.100 0.000 206.072 206.072
0.050 0.000 206.072 206.072
0.020 0.000 206.072 206.072
0.010 0.000 206.072 206.072
Chi-Square Test of Model Fit for the Binary and Ordered Categorical
(Ordinal) Outcomes
Pearson Chi-Square
Mean 0.000
Std Dev 0.000
Degrees of freedom 0
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 0.000 0.000
0.980 0.000 0.000 0.000
0.950 0.000 0.000 0.000
0.900 0.000 0.000 0.000
0.800 0.000 0.000 0.000
0.700 0.000 0.000 0.000
0.500 0.000 0.000 0.000
0.300 0.000 0.000 0.000
0.200 0.000 0.000 0.000
0.100 0.000 0.000 0.000
0.050 0.000 0.000 0.000
0.020 0.000 0.000 0.000
0.010 0.000 0.000 0.000
Likelihood Ratio Chi-Square
Mean 0.000
Std Dev 0.000
Degrees of freedom 0
Number of successful computations 1
Proportions Percentiles
Expected Observed Expected Observed
0.990 0.000 0.000 0.000
0.980 0.000 0.000 0.000
0.950 0.000 0.000 0.000
0.900 0.000 0.000 0.000
0.800 0.000 0.000 0.000
0.700 0.000 0.000 0.000
0.500 0.000 0.000 0.000
0.300 0.000 0.000 0.000
0.200 0.000 0.000 0.000
0.100 0.000 0.000 0.000
0.050 0.000 0.000 0.000
0.020 0.000 0.000 0.000
0.010 0.000 0.000 0.000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THE ESTIMATED MODEL
Latent
Classes
1 229.99999 0.46000
2 270.00001 0.54000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON ESTIMATED POSTERIOR PROBABILITIES
Latent
Classes
1 229.99999 0.46000
2 270.00001 0.54000
FINAL CLASS COUNTS AND PROPORTIONS FOR THE LATENT CLASSES
BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP
Class Counts and Proportions
Latent
Classes
1 230 0.46000
2 270 0.54000
CLASSIFICATION QUALITY
Entropy 1.000
Average Latent Class Probabilities for Most Likely Latent Class Membership (Row)
by Latent Class (Column)
1 2
1 1.000 0.000
2 0.000 1.000
Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 1.000 0.000
2 0.000 1.000
Logits for the Classification Probabilities for the Most Likely Latent Class Membership (Column)
by Latent Class (Row)
1 2
1 13.816 0.000
2 -13.816 0.000
MODEL RESULTS
ESTIMATES S. E. M. S. E. 95% % Sig
Population Average Std. Dev. Average Cover Coeff
Latent Class 1
T ON
X 0.500 0.5408 0.0000 0.0570 0.0017 1.000 1.000
Intercepts
T 0.000 0.0000 0.0000 0.0000 0.0000 1.000 0.000
Thresholds
U$1 15.000 15.0000 0.0000 0.0000 0.0000 1.000 0.000
Latent Class 2
T ON
X 0.500 0.5408 0.0000 0.0570 0.0017 1.000 1.000
Intercepts
T 1.000 0.9738 0.0000 0.1170 0.0007 1.000 1.000
Thresholds
U$1 -15.000 -15.0000 0.0000 0.0000 0.0000 1.000 0.000
Categorical Latent Variables
Means
C#1 0.000 -0.1603 0.0000 0.0897 0.0257 1.000 0.000
QUALITY OF NUMERICAL RESULTS
Average Condition Number for the Information Matrix 0.222E+00
(ratio of smallest to largest eigenvalue)
TECHNICAL 1 OUTPUT
PARAMETER SPECIFICATION FOR LATENT CLASS 1
NU
X
________
0
LAMBDA
X
________
X 0
THETA
X
________
X 0
ALPHA
X
________
0
BETA
X
________
X 0
PSI
X
________
X 0
PARAMETER SPECIFICATION FOR LATENT CLASS 2
NU
X
________
0
LAMBDA
X
________
X 0
THETA
X
________
X 0
ALPHA
X
________
0
BETA
X
________
X 0
PSI
X
________
X 0
PARAMETER SPECIFICATION FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U$1
________
0
TAU(U) FOR LATENT CLASS 2
U$1
________
0
PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
1 0
GAMMA(C)
X
________
C#1 0
C#2 0
PARAMETER SPECIFICATION FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
T#1 T
________ ________
0 0
KAPPA(P) FOR LATENT CLASS 1
X
________
T#1 0
T 2
NU(P) FOR LATENT CLASS 2
T#1 T
________ ________
0 3
KAPPA(P) FOR LATENT CLASS 2
X
________
T#1 0
T 2
STARTING VALUES FOR LATENT CLASS 1
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 0.500
STARTING VALUES FOR LATENT CLASS 2
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 0.500
STARTING VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U$1
________
15.000
TAU(U) FOR LATENT CLASS 2
U$1
________
-15.000
STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
GAMMA(C)
X
________
C#1 0.000
C#2 0.000
STARTING VALUES FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
T#1 T
________ ________
-20.000 0.000
KAPPA(P) FOR LATENT CLASS 1
X
________
T#1 0.000
T 0.500
NU(P) FOR LATENT CLASS 2
T#1 T
________ ________
-20.000 1.000
KAPPA(P) FOR LATENT CLASS 2
X
________
T#1 0.000
T 0.500
POPULATION VALUES FOR LATENT CLASS 1
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 1.000
POPULATION VALUES FOR LATENT CLASS 2
NU
X
________
0.000
LAMBDA
X
________
X 1.000
THETA
X
________
X 0.000
ALPHA
X
________
0.000
BETA
X
________
X 0.000
PSI
X
________
X 1.000
POPULATION VALUES FOR LATENT CLASS INDICATOR MODEL PART
TAU(U) FOR LATENT CLASS 1
U$1
________
15.000
TAU(U) FOR LATENT CLASS 2
U$1
________
-15.000
POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART
ALPHA(C)
C#1 C#2
________ ________
0.000 0.000
GAMMA(C)
X
________
C#1 0.000
C#2 0.000
POPULATION VALUES FOR THE CENSORED/NOMINAL/COUNT MODEL PART
NU(P) FOR LATENT CLASS 1
T#1 T
________ ________
-20.000 0.000
KAPPA(P) FOR LATENT CLASS 1
X
________
T#1 0.000
T 0.500
NU(P) FOR LATENT CLASS 2
T#1 T
________ ________
-20.000 1.000
KAPPA(P) FOR LATENT CLASS 2
X
________
T#1 0.000
T 0.500
POPULATION VALUES FOR THE BASE HAZARD PARAMETERS
BASE HAZARD PARAMETERS FOR LATENT CLASS 1
T#1
________
1.000
BASE HAZARD PARAMETERS FOR LATENT CLASS 2
T#1
________
1.000
TECHNICAL 9 OUTPUT
Error messages for each replication (if any)
SAVEDATA INFORMATION
Order of variables
T
U
X
_TCENT
C
Save file
ex7.30.dat
Save file format Free
Save file record length 10000
Beginning Time: 22:24:38
Ending Time: 22:24:38
Elapsed Time: 00:00:00
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