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Activity Number: 252
Type: Contributed
Date/Time: Monday, August 5, 2013 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #307965
Title: PHM and Logistic Regression Model Using Time-Dependent Covariates for Survival Analysis
Author(s): Alexandre Mendes*+ and Nasser Fard
Companies: Northeastern University and Northeastern University
Keywords: PHM ; proportional hazard model ; Logistic regression ; time-dependent covariates ; failure modes ; survival models
Abstract:

Distribution of survival models are written in terms of the hazard function. Parametric models require specified estimate of the hazard function and do not accommodate for time-dependent covariates. This study addresses time-dependent covariates and compares the proportional hazard model (PHM) to the logistic regression. The experimental design was developed for repeated events with competing failure modes, requiring evaluation for the bias of standard errors and test statistics. The PHM handles time-dependent covariates well; however additional variables need to be incorporated to the model to address constant time-covariate interactions. It also requires testing covariates for proportionality and linearity assumptions. The logistic regression ignores the information on timing of the events; which is corrected by breaking each subject survival history into a set of discrete time intervals that are treated as distinct observations evaluated to a binary distribution. Repeated events can be addressed by both methods with proper correction for lack of heterogeneity. PHM showed more options to address dependence and easier interpretation of the results than the logistic regression.


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