Generalizing observational study results: applying propensity score methods to complex surveys.

ABSTRACT:

Objective

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

Data sources

Simulated data and the 2008 Medical Expenditure Panel Survey.

Study design

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.

Principal findings

In

SUBMITTER: Dugoff EH 

PROVIDER: S-EPMC3894255 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature