Cited by 4,495Advanced Causal Inference TechniquesMeta-analysis and systematic reviewsStatistical Methods in Clinical Trials

Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies

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Austin, Peter C., Stuart, Elizabeth A.·2015·Statistics in Medicine

Abstract

The propensity score is defined as a subject's probability of treatment selection, conditional on observed baseline covariates. Weighting subjects by the inverse probability of treatment received creates a synthetic sample in which treatment assignment is independent of measured baseline covariates. Inverse probability of treatment weighting (IPTW) using the propensity score allows one to obtain unbiased estimates of average treatment effects. However, these estimates are only valid if there are no residual systematic differences in observed baseline characteristics between treated and control subjects in the sample weighted by the estimated inverse probability of treatment. We report on a systematic literature review, in which we found that the use of IPTW has increased rapidly in recent years, but that in the most recent year, a majority of studies did not formally examine whether weighting balanced measured covariates between treatment groups. We then proceed to describe a suite of quantitative and qualitative methods that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample. The quantitative methods use the weighted standardized difference to compare means, prevalences, higher‐order moments, and interactions. The qualitative methods employ graphical methods to compare the distribution of continuous baseline covariates between treated and control subjects in the weighted sample. Finally, we illustrate the application of these methods in an empirical case study. We propose a formal set of balance diagnostics that contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data. © 2015 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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Authors
Austin, P. C., & Stuart, E. A.
Affiliations
  • University of Toronto (CA)
  • Johns Hopkins University (US)
Corresponding author
Austin, Peter C.
Venue
Statistics in Medicine
Publisher
Wiley
Published
2015-08-03
Volume
34
Issue
28
Pages
3661-3679
ISSN
  • 0277-6715 (ISSN-L)
  • 1097-0258
OpenAlex
W2110818436
Cited by
4,495
References
60
Citations by year
2026: 563 · 2025: 565 · 2024: 530 · 2023: 537 · 2022: 651 · 2021: 544 · 2020: 426 · 2019: 288 · 2018: 209 · 2017: 119 · 2016: 53 · 2015: 2 · 2013: 2
FWCI
21.37
Type
journal article
Language
en
Open access
Locations
  • Statistics in Medicine (publishedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by-nc-nd)
  • PubMed (publishedVersion)
Funders
  • (ICES)
  • (MOHLTC)
  • (CIHR) (86508, R01MH099010)
Topics
  • Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
  • Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
  • Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Clinical Trials
Keywords
inverse probability of treatment weighting, propensity score, causal treatment effects, observational studies, covariate balance, graphical method, best practices
MeSH terms
Humans, Monte Carlo Method, Models, Statistical, Outcome Assessment, Health Care, Propensity Score, Observational Studies as Topic

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