scholarly journals A Proportional Hazards Model with Time-dependent Covariates and Time-varying Effects for Analysis of Fetal and Infant Death

2004 ◽  
Vol 160 (3) ◽  
pp. 199-206 ◽  
Author(s):  
R. W. Platt
Keyword(s):  
Infant Death ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Time Dependent ◽  
Time Varying ◽  
Hazards Model ◽  
Time Dependent Covariates

The Joint Analysis of Survival Times and Response Rates in Clinical Trials on the Treatment of Cancer

1980 ◽  
Vol 19 (02) ◽  
pp. 112-114 ◽  
Author(s):  
J. Wahrendorf
Keyword(s):  
Clinical Trial ◽  
Survival Data ◽  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Time Dependent ◽  
Joint Analysis ◽  
Survival Times ◽  
Logrank Test ◽  
Hazards Model ◽  
Time Dependent Covariates

A method is outlined which allows to analyse survival and response data from a clinical trial on the treatment of cancer when the question of interest is whether the somehow defined response to the treatment has an influence on patients’ survival times. This can be done by making use of Cox’s proportional hazards model for survival data together with time-dependent covariates. This allows an appropriate classification of non-respondors and responders by considering the time of diagnosis of response during follow-up. The resulting test can be viewed as a time-dependent logrank test. An example from a clinical trial on gastrointestinal cancer is considered.

The Identifiability of the Mixed Proportional Hazards Model with Time-Varying Coefficients

1996 ◽  
Vol 12 (4) ◽  
pp. 733-738 ◽  
Author(s):  
Brian P. McCall
Keyword(s):  
Proportional Hazards ◽  
Proportional Hazards Model ◽  
Time Varying ◽  
Varying Coefficients ◽  
Hazards Model ◽  
Time Varying Coefficients ◽  
Mixed Proportional Hazards

This paper establishes conditions for the nonparametric identifiability of the mixed proportional hazards model with time-varying coefficients. Unlike the mixed proportional hazards model, a regressor with two distinct values is not sufficient to identify this model. An unbounded regressor, however, is sufficient for identification.

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