Propensity score matching and inverse probability of treatment weighting to address confounding by indication in comparative effectiveness research of oral anticoagulants

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Abstract

After decades of warfarin being the only oral anticoagulant (OAC) widely available for stroke prevention in atrial fibrillation, four direct OACs (apixaban, dabigatran, edoxaban and rivaroxaban) were approved after demonstrating noninferior efficacy and safety versus warfarin in randomized controlled trials. Comparative effectiveness research of OACs based on real-world data provides complementary information to randomized controlled trials. Propensity score matching and inverse probability of treatment weighting are increasingly popular methods used to address confounding by indication potentially arising in comparative effectiveness research due to a lack of randomization in treatment assignment. This review describes the fundamentals of propensity score matching and inverse probability of treatment weighting, appraises differences between them and presents applied examples to elevate understanding of these methods within the atrial fibrillation field.

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ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions

Jonathan AC Sterne, Miguel A. Hernán, Barnaby Reeves … (2018)

Non-randomised studies of the effects of interventions are critical to many areas of healthcare evaluation, but their results may be biased. It is therefore important to understand and appraise their strengths and weaknesses. We developed ROBINS-I (“Risk Of Bias In Non-randomised Studies - of Interventions”), a new tool for evaluating risk of bias in estimates of the comparative effectiveness (harm or benefit) of interventions from studies that did not use randomisation to allocate units (individuals or clusters of individuals) to comparison groups. The tool will be particularly useful to those undertaking systematic reviews that include non-randomised studies.

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An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies

Peter C Austin (2011)

The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.