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Modeling Clustered, Discrete, or Grouped Time Survival Data with Covariates
Author(s) -
Ross Eric A.,
Moore Dirk
Publication year - 1999
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.0006-341x.1999.00813.x
Subject(s) - covariate , statistics , estimating equations , marginal model , proportional hazards model , generalized estimating equation , mathematics , generalized linear model , econometrics , hazard , accelerated failure time model , cluster (spacecraft) , hazard ratio , survival analysis , regression analysis , confidence interval , computer science , maximum likelihood , chemistry , organic chemistry , programming language
Summary. We have developed methods for modeling discrete or grouped time, right‐censored survival data collected from correlated groups or clusters. We assume that the marginal hazard of failure for individual items within a cluster is specified by a linear log odds survival model and the dependence structure is based on a gamma frailty model. The dependence can be modeled as a function of cluster‐level covariates. Likelihood equations for estimating the model parameters are provided. Generalized estimating equations for the marginal hazard regression parameters and pseudolikelihood methods for estimating the dependence parameters are also described. Data from two clinical trials are used for illustration purposes.