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Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies.
Stat Med. 2015 Dec 10; 34(28):3661-79.SM

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.

Authors+Show Affiliations

Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada. Schulich Heart Research Program, Sunnybrook Research Institute, Toronto, Canada.Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A. Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A. Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, U.S.A.

Pub Type(s)

Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Review
Systematic Review

Language

eng

PubMed ID

26238958

Citation

Austin, Peter C., and Elizabeth A. Stuart. "Moving Towards Best Practice when Using Inverse Probability of Treatment Weighting (IPTW) Using the Propensity Score to Estimate Causal Treatment Effects in Observational Studies." Statistics in Medicine, vol. 34, no. 28, 2015, pp. 3661-79.
Austin PC, Stuart EA. Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Stat Med. 2015;34(28):3661-79.
Austin, P. C., & Stuart, E. A. (2015). Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. Statistics in Medicine, 34(28), 3661-79. https://doi.org/10.1002/sim.6607
Austin PC, Stuart EA. Moving Towards Best Practice when Using Inverse Probability of Treatment Weighting (IPTW) Using the Propensity Score to Estimate Causal Treatment Effects in Observational Studies. Stat Med. 2015 Dec 10;34(28):3661-79. PubMed PMID: 26238958.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies. AU - Austin,Peter C, AU - Stuart,Elizabeth A, Y1 - 2015/08/03/ PY - 2015/04/08/received PY - 2015/06/16/revised PY - 2015/07/09/accepted PY - 2015/8/5/entrez PY - 2015/8/5/pubmed PY - 2016/8/16/medline KW - IPTW KW - causal inference KW - inverse probability of treatment weighting KW - observational study KW - propensity score SP - 3661 EP - 79 JF - Statistics in medicine JO - Stat Med VL - 34 IS - 28 N2 - 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. SN - 1097-0258 UR - https://www.unboundmedicine.com/medline/citation/26238958/Moving_towards_best_practice_when_using_inverse_probability_of_treatment_weighting__IPTW__using_the_propensity_score_to_estimate_causal_treatment_effects_in_observational_studies_ L2 - https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/26238958/ DB - PRIME DP - Unbound Medicine ER -