Illustration of 2 Fusion Designs and Estimators

Fusion study designs combine data from different sources to answer questions that could not be answered (as well) by subsets of the data. Studies that augment main study data with validation data, as in measurement-error correction studies or generalizability studies, are examples of fusion designs. Fusion estimators, here solutions to stacked estimating functions, produce consistent answers to identified research questions using data from fusion designs. In this paper, we describe a pair of examples of fusion designs and estimators, one where we generalize a proportion to a target population and one where we correct measurement error in a proportion. For each case, we present an example motivated by human immunodeficiency virus research and summarize results from simulation studies. Simulations demonstrate that the fusion estimators provide approximately unbiased results with appropriate 95% confidence interval coverage. Fusion estimators can be used to appropriately combine data in answering important questions that benefit from multiple sources of information.

Automated summary

Fusion study designs combine data from different sources to effectively answer research questions that cannot be addressed by subsets of the data alone. Fusion estimators, such as stacked estimating functions, provide consistent and unbiased results for identified research questions using data from fusion designs. This paper presents examples of fusion designs and estimators, demonstrating their applications in generalizing proportions to target populations and correcting measurement errors in proportions, with simulations showing their effectiveness and accuracy.