Ten things to remember about propensity scores

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Abstract

Propensity score methods are popular to control for confounding in observational biomedical studies of risk factors or medical treatments. This paper focused on aspects of propensity score methods that often remain undiscussed, including unmeasured confounding, missing data, variable selection, statistical efficiency, estimands, the positivity assumption, and predictive performance of the propensity score model.

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Most cited references 13

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The central role of the propensity score in observational studies for causal effects

Paul R. Rosenbaum, Donald B Rubin (1983)

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Reporting and Guidelines in Propensity Score Analysis: A Systematic Review of Cancer and Cancer Surgical Studies.

Xiaoxin I Yao, Xiaofei Wang, Paul J. Speicher … (2017)

: Propensity score (PS) analysis is increasingly being used in observational studies, especially in some cancer studies where random assignment is not feasible. This systematic review evaluates the use and reporting quality of PS analysis in oncology studies.

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Introduction to propensity scores.

Elizabeth Williamson, Andrew Forbes (2014)

Although randomization provides a gold-standard method of assessing causal relationships, it is not always possible to randomly allocate exposures. Where exposures are not randomized, estimating exposure effects is complicated by confounding. The traditional approach to dealing with confounding is to adjust for measured confounding variables within a regression model for the outcome variable. An alternative approach--propensity scoring--instead fits a regression model to the exposure variable. For a binary exposure, the propensity score is the probability of being exposed, given the measured confounders. These scores can be estimated from the data, for example by fitting a logistic regression model for the exposure including the confounders as explanatory variables and obtaining the estimated propensity scores from the predicted exposure probabilities from this model. These estimated propensity scores can then be used in various ways-matching, stratification, covariate-adjustment or inverse-probability weighting-to obtain estimates of the exposure effect. In this paper, we provide an introduction to propensity score methodology and review its use within respiratory health research. We illustrate propensity score methods by investigating the research question: 'Does personal smoking affect the risk of subsequent asthma?' using data taken from the Tasmanian Longitudinal Health Study.