Journal
HEALTH SERVICES RESEARCH
Volume 49, Issue 5, Pages 1701-1720Publisher
WILEY-BLACKWELL
DOI: 10.1111/1475-6773.12182
Keywords
Observational data/quasi-experiments; administrative data uses; patient outcomes/function
Funding
- NCI/NINR [5R01CA116227]
- National Palliative Care Research Center
- Department of Veterans Affairs HSRD CDA [11-201/CDP 12-255]
- National Institute on Aging [1K23AG040774-01A1, K24 AG022345]
- American Federation for Aging Research
- Doris Duke Charitable Foundation Clinical Research Program
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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.
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