Just what the doctor ordered: An evaluation of provider preference-based Instrumental Variable methods in observational studies, with application for comparative effectiveness of type 2 diabetes therapy

TL;DR

This paper evaluates various methods for constructing surrogate instruments based on provider prescription preferences in observational studies, demonstrating their effectiveness in estimating causal effects in healthcare data, especially for diabetes treatments.

Contribution

It introduces a novel model-based surrogate construction method and provides a comprehensive simulation study comparing different approaches under various data conditions.

Findings

Preference-based IV methods can effectively estimate causal effects in observational health data.

The proposed model-based method reduces bias when prescription preferences change over time.

Choice of surrogate construction depends on data availability and missingness conditions.

Abstract

Instrumental Variables provide a way of addressing bias due to unmeasured confounding when estimating treatment effects using observational data. As instrument prescription preference of individual healthcare providers has been proposed. Because prescription preference is hard to measure and often unobserved, a surrogate measure constructed from available data is often required for the analysis. Different construction methods for this surrogate measure are possible, such as simple rule-based methods which make use of the observed treatment patterns, or more complex model-based methods that employ formal statistical models to explain the treatment behaviour whilst considering measured confounders. The choice of construction method relies on aspects like data availability within provider, missing data in measured confounders, and possible changes in prescription preference over time. In this paper we conduct a comprehensive simulation study to evaluate different construction methods for surrogates of prescription preference under different data conditions, including: different provider sizes, missing covariate data, and change in preference. We also propose a novel model-based construction method to address between provider differences and change in prescription preference. All presented construction methods are exemplified in a case study of the relative glucose lowering effect of two type 2 diabetes treatments in observational data. Our study shows that preference-based Instrumental Variable methods can be a useful tool for causal inference from observational health data. The choice of construction method should be driven by the data condition at hand. Our proposed method is capable of estimating the causal treatment effect without bias in case of sufficient prescription data per provider, changing prescription preference over time and non-ignorable missingness in measured confounders.