Covariate adjustment using propensity scores for dependent censoring problems in the accelerated failure time model.

ABSTRACT: In many medical studies, estimation of the association between treatment and outcome of interest is often of primary scientific interest. Standard methods for its evaluation in survival analysis typically require the assumption of independent censoring. This assumption might be invalid in many medical studies, where the presence of dependent censoring leads to difficulties in analyzing covariate effects on disease outcomes. This data structure is called "semicompeting risks data," for which many authors have proposed an artificial censoring technique. However, confounders with large variability may lead to excessive artificial censoring, which subsequently results in numerically unstable estimation. In this paper, we propose a strategy for weighted estimation of the associations in the acc

SUBMITTER: Cho Y 

PROVIDER: S-EPMC5768472 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature