Nonproportional hazards and unobserved heterogeneity in clustered survival data: When can we tell the difference?

ABSTRACT: Multivariate survival data are frequently encountered in biomedical applications in the form of clustered failures (or recurrent events data). A popular way of analyzing such data is by using shared frailty models, which assume that the proportional hazards assumption holds conditional on an unobserved cluster-specific random effect. Such models are often incorporated in more complicated joint models in survival analysis. If the random effect distribution has finite expectation, then the conditional proportional hazards assumption does not carry over to the marginal models. It has been shown that, for univariate data, this makes it impossible to distinguish between the presence of unobserved heterogeneity (eg, due to missing covariates) and marginal nonproportional hazards. We show that ti

SUBMITTER: Balan TA 

PROVIDER: S-EPMC6619282 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature