Analysis of the Proportional Hazards Model with Sparse Longitudinal Covariates.

ABSTRACT: Regression analysis of censored failure observations via the proportional hazards model permits time-varying covariates which are observed at death times. In practice, such longitudinal covariates are typically sparse and only measured at infrequent and irregularly spaced follow-up times. Full likelihood analyses of joint models for longitudinal and survival data impose stringent modelling assumptions which are difficult to verify in practice and which are complicated both inferentially and computationally. In this article, a simple kernel weighted score function is proposed with minimal assumptions. Two scenarios are considered: half kernel estimation in which observation ceases at the time of the event and full kernel estimation for data where observation may continue after the event, as

SUBMITTER: Cao H 

PROVIDER: S-EPMC4643320 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature