Generalizing Observational Study Results: Applying Propensity Score Methods to Complex Surveys

<jats:sec><jats:title>Objective</jats:title><jats:p>To provide a tutorial for using propensity score methods with complex survey data.</jats:p></jats:sec><jats:sec><jats:title>Data Sources</jats:title><jats:p>Simulated data and the 2008 Medical Expenditure Panel Survey.</jats:p></jats:sec><jats:sec><jats:title>Study Design</jats:title><jats:p>Using simulation, we compared the following methods for estimating the treatment effect: a naïve estimate (ignoring both survey weights and propensity scores), survey weighting, propensity score methods (nearest neighbor matching, weighting, and subclassification), and propensity score methods in combination with survey weighting. Methods are compared in terms of bias and 95 percent confidence interval coverage. In Example 2, we used these methods to estimate the effect on health care spending of having a generalist versus a specialist as a usual source of care.</jats:p></jats:sec><jats:sec><jats:title>Principal Findings</jats:title><jats:p>In general, combining a propensity score method and survey weighting is necessary to achieve unbiased treatment effect estimates that are generalizable to the original survey target population.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Propensity score methods are an essential tool for addressing confounding in observational studies. Ignoring survey weights may lead to results that are not generalizable to the survey target population. This paper clarifies the appropriate inferences for different propensity score methods and suggests guidelines for selecting an appropriate propensity score method based on a researcher's goal.</jats:p></jats:sec>