Mplus VERSION 7
MUTHEN & MUTHEN
09/22/2012  10:55 PM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a continuous-time
   	survival analysis using the Cox regression model
  DATA:   FILE = ex6.20.dat;
  VARIABLE:	NAMES = t x tc;
  	SURVIVAL = t (ALL);
  	TIMECENSORED = tc (0 = NOT 1 = RIGHT);
  ANALYSIS:	BASEHAZARD = OFF;
  MODEL:	t ON x;



INPUT READING TERMINATED NORMALLY



this is an example of a continuous-time
survival analysis using the Cox regression model

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                          50

Number of dependent variables                                    1
Number of independent variables                                  1
Number of continuous latent variables                            0

Observed dependent variables

  Time-to-event (survival)
   T

Observed independent variables
   X

Variables with special functions

  Time-censoring variables
   TC


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
Optimization algorithm                                         EMA
Integration Specifications
  Type                                                    STANDARD
  Number of integration points                                  15
  Dimensions of numerical integration                            0
  Adaptive quadrature                                           ON
Base Hazard                                                    OFF
Cholesky                                                       OFF

Input data file(s)
  ex6.20.dat
Input data format  FREE



THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        1

Loglikelihood

          H0 Value                           5.099
          H0 Scaling Correction Factor      1.2492
            for MLR

Information Criteria

          Akaike (AIC)                      -8.197
          Bayesian (BIC)                    -6.285
          Sample-Size Adjusted BIC          -9.424
            (n* = (n + 2) / 24)



MODEL RESULTS

                                                    Two-Tailed
                    Estimate       S.E.  Est./S.E.    P-Value

 T          ON
    X                  0.494      0.262      1.882      0.060


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.100E+01
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  22:55:58
        Ending Time:  22:55:58
       Elapsed Time:  00:00:00



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) 1998-2012 Muthen & Muthen

Back to examples