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To provide a tutorial for using propensity score methods with complex survey data.

Simulated data and the 2008 Medical Expenditure Panel Survey.

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.

In general, combining a propensity score method and survey weighting is necessary to achieve unbiased treatment effect estimates that …