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Methods for Reducing Selection Bias in Health Economic Evaluation

Cost-effectiveness analyses (CEA) commonly use observational studies, either alongside or instead of data from randomized controlled trials (RCTs). A major concern is that the results suffer from treatment selection bias due to confounding variables that influence both treatment and outcome. Commonly used analytical methods for dealing with selection bias such as regression analysis or propensity score (Pscore) matching can be highly sensitive to model specification. Methodological research investigating alternative approaches including a matching method, Genetic Matching (GenMatch) , is undertaken. This multivariate matching method extends Pscore matching by using an automated search algorithm to optimise balance on baseline covariates, given the data. In the context of CEA where a major concern is balancing baseline covariates, GenMatch can reduce bias compared to propensity score matching.

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