Propensity scores: From naive enthusiasm to intuitive understanding


Williamson, E; Morley, R; Lucas, A; Carpenter, J; (2011) Propensity scores: From naive enthusiasm to intuitive understanding. Statistical methods in medical research, 21 (3). pp. 273-293. ISSN 0962-2802 DOI: https://doi.org/10.1177/0962280210394483

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Abstract

Estimation of the effect of a binary exposure on an outcome in the presence of confounding is often carried out via outcome regression modelling. An alternative approach is to use propensity score methodology. The propensity score is the conditional probability of receiving the exposure given the observed covariates and can be used, under the assumption of no unmeasured confounders, to estimate the causal effect of the exposure. In this article, we provide a non-technical and intuitive discussion of propensity score methodology, motivating the use of the propensity score approach by analogy with randomised studies, and describe the four main ways in which this methodology can be implemented. We carefully describe the population parameters being estimated - an issue that is frequently overlooked in the medical literature. We illustrate these four methods using data from a study investigating the association between maternal choice to provide breast milk and the infant's subsequent neurodevelopment. We outline useful extensions of propensity score methodology and discuss directions for future research. Propensity score methods remain controversial and there is no consensus as to when, if ever, they should be used in place of traditional outcome regression models. We therefore end with a discussion of the relative advantages and disadvantages of each.

Item Type: Article
Keywords: confounding, inverse probability weighting, matching, observational, study, propensity score, stratification, statistics-in-medicine, marginal odds ratios, critical-appraisal, variable selection, causal inference, bias reduction, monte-carlo, regression, models, performance
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Research Centre: Centre for Statistical Methodology
PubMed ID: 21262780
Web of Science ID: 304231900005
URI: http://researchonline.lshtm.ac.uk/id/eprint/37449

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