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

08/15/2023
by   Laura M. Güdemann, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/17/2020

Confounding Feature Acquisition for Causal Effect Estimation

Reliable treatment effect estimation from observational data depends on ...
research
03/30/2016

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index

Estimating the long-term effects of treatments is of interest in many fi...
research
12/20/2021

Predicting treatment effects from observational studies using machine learning methods: A simulation study

Measuring treatment effects in observational studies is challenging beca...
research
12/02/2020

Doubly-robust evaluation of high-dimensional surrogate markers

When evaluating the effectiveness of a treatment, policy, or interventio...
research
07/16/2022

Hypothetical Treatment Accelerations: Estimating Causal Effects of Kidney Transplants from Observational Data

Patients with end-stage kidney disease can expect to wait for several ye...
research
08/01/2022

Model-Twin Randomization (MoTR): A Monte Carlo Method for Estimating the Within-Individual Average Treatment Effect Using Wearable Sensors

Temporally dense single-person "small data" have become widely available...

Please sign up or login with your details

Forgot password? Click here to reset