A Bayesian proportional hazards regression model with non-ignorably missing time-varying covariates.

ABSTRACT: Missing covariate data are common in observational studies of time to an event, especially when covariates are repeatedly measured over time. Failure to account for the missing data can lead to bias or loss of efficiency, especially when the data are non-ignorably missing. Previous work has focused on the case of fixed covariates rather than those that are repeatedly measured over the follow-up period, hence, here we present a selection model that allows for proportional hazards regression with time-varying covariates when some covariates may be non-ignorably missing. We develop a fully Bayesian model and obtain posterior estimates of the parameters via the Gibbs sampler in WinBUGS. We illustrate our model with an analysis of post-diagnosis weight change and survival after breast cancer di

SUBMITTER: Bradshaw PT 

PROVIDER: S-EPMC3253577 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature