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The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies

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  • Peter C. Austin

    (Institute for Clinical Evaluative Sciences, Toronto, Ontario, Canada, peter.austin@ices.on.ca, Dalla Lana School of Public Health, University of Toronto, Department of Health Management, Policy and Evaluation, University of Toronto)

Abstract

The propensity score is a balancing score: conditional on the propensity score, treated and untreated subjects have the same distribution of observed baseline characteristics. Four methods of using the propensity score have been described in the literature: stratification on the propensity score, propensity score matching, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. However, the relative ability of these methods to reduce systematic differences between treated and untreated subjects has not been examined. The authors used an empirical case study and Monte Carlo simulations to examine the relative ability of the 4 methods to balance baseline covariates between treated and untreated subjects. They used standardized differences in the propensity score matched sample and in the weighted sample. For stratification on the propensity score, within-quintile standardized differences were computed comparing the distribution of baseline covariates between treated and untreated subjects within the same quintile of the propensity score. These quintile-specific standardized differences were then averaged across the quintiles. For covariate adjustment, the authors used the weighted conditional standardized absolute difference to compare balance between treated and untreated subjects. In both the empirical case study and in the Monte Carlo simulations, they found that matching on the propensity score and weighting using the inverse probability of treatment eliminated a greater degree of the systematic differences between treated and untreated subjects compared with the other 2 methods. In the Monte Carlo simulations, propensity score matching tended to have either comparable or marginally superior performance compared with propensity-score weighting.

Suggested Citation

  • Peter C. Austin, 2009. "The Relative Ability of Different Propensity Score Methods to Balance Measured Covariates Between Treated and Untreated Subjects in Observational Studies," Medical Decision Making, , vol. 29(6), pages 661-677, November.
  • Handle: RePEc:sae:medema:v:29:y:2009:i:6:p:661-677
    DOI: 10.1177/0272989X09341755
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    Cited by:

    1. Kebede, Dereje & Emana, Bezabih & Tesfay, Girmay, 2023. "Impact of land acquisition for large-scale agricultural investments on food security status of displaced households: The case of Ethiopia," Land Use Policy, Elsevier, vol. 126(C).
    2. Daisuke Kato & Ichiro Kawachi & Naoki Kondo, 2022. "Complex Multimorbidity and Working beyond Retirement Age in Japan: A Prospective Propensity-Matched Analysis," IJERPH, MDPI, vol. 19(11), pages 1-10, May.
    3. Ye Zhang & Ulf-G. Gerdtham & Helena Rydell & Johan Jarl, 2020. "Quantifying the Treatment Effect of Kidney Transplantation Relative to Dialysis on Survival Time: New Results Based on Propensity Score Weighting and Longitudinal Observational Data from Sweden," IJERPH, MDPI, vol. 17(19), pages 1-13, October.
    4. Stephanie L Mayne & Brian K Lee & Amy H Auchincloss, 2015. "Evaluating Propensity Score Methods in a Quasi-Experimental Study of the Impact of Menu-Labeling," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-12, December.
    5. Marion Ravit & Andrainolo Ravalihasy & Martine Audibert & Valery Ridde & Emmanuel Bonnet & Bertille Raffalli & Flore-Apolline Roy & Anais N’landu & Alexandre Dumont, 2020. "The impact of the obstetrical risk insurance scheme in Mauritania on maternal healthcare utilization: a propensity score matching analysis," Post-Print hal-02509190, HAL.

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