Using generalized linear models to implement g-estimation for survival data with time-varying confounding.

ABSTRACT: Using data from observational studies to estimate the causal effect of a time-varying exposure, repeatedly measured over time, on an outcome of interest requires careful adjustment for confounding. Standard regression adjustment for observed time-varying confounders is unsuitable, as it can eliminate part of the causal effect and induce bias. Inverse probability weighting, g-computation, and g-estimation have been proposed as being more suitable methods. G-estimation has some advantages over the other two methods, but until recently there has been a lack of flexible g-estimation methods for a survival time outcome. The recently proposed Structural Nested Cumulative Survival Time Model (SNCSTM) is such a method. Efficient estimation of the parameters of this model required bespoke software.

SUBMITTER: Seaman SR 

PROVIDER: S-EPMC7612171 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature