Title:      A New Non-Parametric Matching Method for Bias
Adjustment with Applications to Economic Evaluations

Authors:    Jasjeet Sekhon

Entrydate:  2008-05-11 23:56:50

Keywords:   semiparametric and nonparametric matching methods,
observational studies, randomized controlled trials, health
economic evaluation 

Abstract:   In health economic studies that use observational
data, a key concern is how to adjust for imbalances in baseline
covariates due to the non-random assignment of the programs
under evaluation. Traditional methods of covariate adjustment
such as regression and propensity score matching are model
dependent and often fail to replicate the results of randomized
controlled trials.  We demonstrate a new non-parametric matching
method, Genetic Matching, which is a generalization of propensity
score and Mahalanobis distance matching, using two contrasting
case studies.  In the first, an economic evaluation of a
clinical intervention (Pulmonary Artery Catheterization),
applying Genetic Matching to observational data replicates the
substantive results of a corresponding randomized controlled
trial unlike the extant literature.  And in the second case
study evaluating capitation versus fee-for service, Genetic
Matching radically improves balance on baseline covariates and
overturns previous conclusions based on traditional methods.

http://polmeth.wustl.edu/retrieve.php?id=745

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