Selection bias when using instrumental variable methods to compare two treatments but more than two treatments are available

TL;DR

This paper investigates how preselection based on treatments can bias instrumental variable estimates in observational studies with multiple treatments, proposing a method to address this bias.

Contribution

It introduces a new procedure to identify treatment effects accounting for selection bias in IV analyses with more than two treatments.

Findings

The proposed method reduces bias in simulated data.

Application to THIN database illustrates practical utility.

Identifies conditions where bias is minimized.

Abstract

Instrumental variable (IV) methods are widely used to adjust for the bias in estimating treatment effects caused by unmeasured confounders in observational studies. In this manuscript, we provide empirical and theoretical evidence that the IV methods may result in biased treatment effects if applied on a data set in which subjects are preselected based on their received treatments. We frame this as a selection bias problem and propose a procedure that identifies the treatment effect of interest as a function of a vector of sensitivity parameters. We also list assumptions under which analyzing the preselected data does not lead to a biased treatment effect estimate. The performance of the proposed method is examined using simulation studies. We applied our method on The Health Improvement Network (THIN) database to estimate the comparative effect of metformin and sulfonylureas on weight gain among diabetic patients.