Mplus VERSION 8.8
MUTHEN & MUTHEN
04/19/2022 11:12 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;
TIMECENSORED = tc (0 = NOT 1 = RIGHT);
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)
Non-parametric
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
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 0.022 0.087 -2.304 2.00% -0.775 -0.312 0.004
50.000 0.968 -0.183 2.384 2.00% 0.171 0.949
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: 23:12:25
Ending Time: 23:12:25
Elapsed Time: 00:00:00
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