Estimation of indirect effect when the mediator is a censored variable.

ABSTRACT: A mediation model explores the direct and indirect effects of an initial variable ( X) on an outcome variable ( Y) by including a mediator ( M). In many realistic scenarios, investigators observe censored data instead of the complete data. Current research in mediation analysis for censored data focuses mainly on censored outcomes, but not censored mediators. In this study, we proposed a strategy based on the accelerated failure time model and a multiple imputation approach. We adapted a measure of the indirect effect for the mediation model with a censored mediator, which can assess the indirect effect at both the group and individual levels. Based on simulation, we established the bias in the estimations of different paths (i.e. the effects of X on M [ a], of M on Y [ b] and of X on Y gi

SUBMITTER: Wang J 

PROVIDER: S-EPMC5500452 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature