Instrumental variable estimation for a time-varying treatment and a time-to-event outcome via structural nested cumulative failure time models.

ABSTRACT:

Background

In many applications of instrumental variable (IV) methods, the treatments of interest are intrinsically time-varying and outcomes of interest are failure time outcomes. A common example is Mendelian randomization (MR), which uses genetic variants as proposed IVs. In this article, we present a novel application of g-estimation of structural nested cumulative failure models (SNCFTMs), which can accommodate multiple measures of a time-varying treatment when modelling a failure time outcome in an IV analysis.

Methods

A SNCFTM models the ratio of two conditional mean counterfactual outcomes at time k under two treatment strategies which differ only at an earlier time m. These models can be extended to accommodate inverse probability of censoring weights, and can be ap

SUBMITTER: Shi J 

PROVIDER: S-EPMC8620657 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature