Mplus VERSION 8
MUTHEN & MUTHEN
04/10/2017   4:53 AM

INPUT INSTRUCTIONS

  TITLE:	this is an example of a two-level
  		continuous-time survival analysis using
  		Cox regression with a random intercept
  DATA:	FILE = ex9.18.dat;
  VARIABLE:	NAMES = t x w tc clus;
  	CLUSTER = clus;
  	WITHIN = x;
  	BETWEEN = w;
  	SURVIVAL = t (ALL);
  	TIMECENSORED = tc (0 = NOT 1 = RIGHT);
  ANALYSIS:	TYPE = TWOLEVEL;
  	BASEHAZARD = OFF;
  MODEL:	%WITHIN%
  	t ON x;
  	%BETWEEN%
  	t ON w;
  	t;



INPUT READING TERMINATED NORMALLY



this is an example of a two-level
continuous-time survival analysis using
Cox regression with a random intercept

SUMMARY OF ANALYSIS

Number of groups                                                 1
Number of observations                                        1000

Number of dependent variables                                    1
Number of independent variables                                  2
Number of continuous latent variables                            0

Observed dependent variables

  Time-to-event (survival)

    Non-parametric
     T

Observed independent variables
   X           W

Variables with special functions

  Cluster variable      CLUS

  Time-censoring variables
   TC

  Within variables
   X

  Between variables
   W


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                            1
  Adaptive quadrature                                           ON
Base Hazard                                                    OFF
Cholesky                                                        ON

Input data file(s)
  ex9.18.dat
Input data format  FREE


SUMMARY OF DATA

     Number of clusters                        110




UNIVARIATE SAMPLE STATISTICS


     UNIVARIATE HIGHER-ORDER MOMENT DESCRIPTIVE STATISTICS

         Variable/         Mean/     Skewness/   Minimum/ % with                Percentiles
        Sample Size      Variance    Kurtosis    Maximum  Min/Max      20%/60%    40%/80%    Median

     X                     0.008      -0.111      -3.990    0.10%      -0.832     -0.259      0.005
            1000.000       1.022      -0.042       2.840    0.10%       0.272      0.881
     W                    -0.101       0.106      -2.354    0.91%      -1.109     -0.363     -0.136
             110.000       1.088      -0.309       2.752    0.91%       0.192      0.742


THE MODEL ESTIMATION TERMINATED NORMALLY



MODEL FIT INFORMATION

Number of Free Parameters                        3

Loglikelihood

          H0 Value                         116.945
          H0 Scaling Correction Factor      1.0042
            for MLR

Information Criteria

          Akaike (AIC)                    -227.890
          Bayesian (BIC)                  -213.167
          Sample-Size Adjusted BIC        -222.695
            (n* = (n + 2) / 24)



MODEL RESULTS

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

Within Level

 T          ON
    X                  0.422      0.043      9.819      0.000

Between Level

 T          ON
    W                  0.203      0.078      2.604      0.009

 Residual Variances
    T                  0.497      0.103      4.801      0.000


QUALITY OF NUMERICAL RESULTS

     Condition Number for the Information Matrix              0.310E+00
       (ratio of smallest to largest eigenvalue)


     Beginning Time:  04:53:47
        Ending Time:  04:53:47
       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) 1998-2017 Muthen & Muthen

Back to examples