A semiparametric estimator for the proportional hazards model with longitudinal covariates measured with error
Top Cited Papers
- 1 June 2001
- journal article
- research article
- Published by Oxford University Press (OUP) in Biometrika
- Vol. 88 (2) , 447-458
- https://doi.org/10.1093/biomet/88.2.447
Abstract
A common objective in longitudinal studies is to characterise the relationship between a failure time process and time‐independent and time‐dependent covariates. Time‐dependent covariates are generally available as longitudinal data collected periodically during the course of the study. We assume that these data follow a linear mixed effects model with normal measurement error and that the hazard of failure depends both on the underlying random effects describing the covariate process and other time‐independent covariates through a proportional hazards relationship. A routine assumption is that the random effects are normally distributed; however, this need not hold in practice. Within this framework, we develop a simple method for estimating the proportional hazards model parameters that requires no assumption on the distribution of the random effects. Large‐sample properties are discussed, and finite‐sample performance is assessed and compared to competing methods via simulation.Keywords
This publication has 0 references indexed in Scilit: