Bias amplification in the g-computation algorithm for time-varying treatments: a case study of industry payments and prescription of opioid products.

ABSTRACT:

Background

It is often challenging to determine which variables need to be included in the g-computation algorithm under the time-varying setting. Conditioning on instrumental variables (IVs) is known to introduce greater bias when there is unmeasured confounding in the point-treatment settings, and this is also true for near-IVs which are weakly associated with the outcome not through the treatment. However, it is unknown whether adjusting for (near-)IVs amplifies bias in the g-computation algorithm estimators for time-varying treatments compared to the estimators ignoring such variables. We thus aimed to compare the magnitude of bias by adjusting for (near-)IVs across their different relationships with treatments in the time-varying settings.

Methods

After showing a case s

SUBMITTER: Inoue K 

PROVIDER: S-EPMC9036763 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature