Methods for constructing and assessing propensity scores

Abstract

Objectives: To model the steps involved in preparing for and carrying out propensity score analyses by providing step-by-step guidance and Stata code applied to an empirical dataset.

Study design: Guidance, Stata code, and empirical examples are given to illustrate (1) the process of choosing variables to include in the propensity score; (2) balance of propensity score across treatment and comparison groups; (3) balance of covariates across treatment and comparison groups within blocks of the propensity score; (4) choice of matching and weighting strategies; (5) balance of covariates after matching or weighting the sample; and (6) interpretation of treatment effect estimates.

Empirical application: We use data from the Palliative Care for Cancer Patients (PC4C) study, a multisite observational study of the effect of inpatient palliative care on patient health outcomes and health services use, to illustrate the development and use of a propensity score.

Conclusions: Propensity scores are one useful tool for accounting for observed differences between treated and comparison groups. Careful testing of propensity scores is required before using them to estimate treatment effects.

Keywords: Observational data/quasi-experiments; administrative data uses; patient outcomes/function.

Figure 1

Distribution of Propensity Score across Treatment and Comparison Groups

Figure 2

Example of Density Plots of Mean Physical Symptom Severity at Baseline before and after Kernel Matching on Empirical Dataset