Mplus VERSION 7.2
MUTHEN & MUTHEN
05/07/2014   2:05 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
              ________
 1              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 CLASS PATTERNS
BASED ON ESTIMATED POSTERIOR PROBABILITIES

    Latent
   Classes

       1        229.99999          0.46000
       2        270.00001          0.54000


CLASSIFICATION QUALITY

     Entropy                         1.000


CLASSIFICATION OF INDIVIDUALS BASED ON THEIR MOST LIKELY LATENT CLASS MEMBERSHIP

Class Counts and Proportions

    Latent
   Classes

       1              230          0.46000
       2              270          0.54000


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
              ________
 1                  0


           LAMBDA
              X
              ________
 X                  0


           THETA
              X
              ________
 X                  0


           ALPHA
              X
              ________
 1                  0


           BETA
              X
              ________
 X                  0


           PSI
              X
              ________
 X                  0


     PARAMETER SPECIFICATION FOR LATENT CLASS 2


           NU
              X
              ________
 1                  0


           LAMBDA
              X
              ________
 X                  0


           THETA
              X
              ________
 X                  0


           ALPHA
              X
              ________
 1                  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
              ________
 1                  0


           TAU(U) FOR LATENT CLASS 2
              U$1
              ________
 1                  0


     PARAMETER SPECIFICATION FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1                  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
              ________      ________
 1                  0             0


           KAPPA(P) FOR LATENT CLASS 1
              X
              ________
 T#1                0
 T                  2


           NU(P) FOR LATENT CLASS 2
              T#1           T
              ________      ________
 1                  0             3


           KAPPA(P) FOR LATENT CLASS 2
              X
              ________
 T#1                0
 T                  2


     STARTING VALUES FOR LATENT CLASS 1


           NU
              X
              ________
 1              0.000


           LAMBDA
              X
              ________
 X              1.000


           THETA
              X
              ________
 X              0.000


           ALPHA
              X
              ________
 1              0.000


           BETA
              X
              ________
 X              0.000


           PSI
              X
              ________
 X              0.500


     STARTING VALUES FOR LATENT CLASS 2


           NU
              X
              ________
 1              0.000


           LAMBDA
              X
              ________
 X              1.000


           THETA
              X
              ________
 X              0.000


           ALPHA
              X
              ________
 1              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
              ________
 1             15.000


           TAU(U) FOR LATENT CLASS 2
              U$1
              ________
 1            -15.000


     STARTING VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1              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
              ________      ________
 1            -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
              ________      ________
 1            -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
              ________
 1              0.000


           LAMBDA
              X
              ________
 X              1.000


           THETA
              X
              ________
 X              0.000


           ALPHA
              X
              ________
 1              0.000


           BETA
              X
              ________
 X              0.000


           PSI
              X
              ________
 X              1.000


     POPULATION VALUES FOR LATENT CLASS 2


           NU
              X
              ________
 1              0.000


           LAMBDA
              X
              ________
 X              1.000


           THETA
              X
              ________
 X              0.000


           ALPHA
              X
              ________
 1              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
              ________
 1             15.000


           TAU(U) FOR LATENT CLASS 2
              U$1
              ________
 1            -15.000


     POPULATION VALUES FOR LATENT CLASS REGRESSION MODEL PART


           ALPHA(C)
              C#1           C#2
              ________      ________
 1              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
              ________      ________
 1            -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
              ________      ________
 1            -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              1.000


           BASE HAZARD PARAMETERS FOR LATENT CLASS 2
              T#1
              ________
 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:  14:05:11
        Ending Time:  14:05:11
       Elapsed Time:  00:00:00



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