Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study |
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Authors: | Chunhao Tu Shuo Jiao Woon Yuen Koh |
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Institution: | 1. College of Pharmacy, University of New England, Portland, ME 04103, USActu@une.edu;3. Division of Public Health, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA;4. Department of Mathematical Sciences, University of New England, Biddeford, ME 04005, USA |
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Abstract: | In observational studies, unbalanced observed covariates between treatment groups often cause biased inferences on the estimation of treatment effects. Recently, generalized propensity score (GPS) has been proposed to overcome this problem; however, a practical technique to apply the GPS is lacking. This study demonstrates how clustering algorithms can be used to group similar subjects based on transformed GPS. We compare four popular clustering algorithms: k-means clustering (KMC), model-based clustering, fuzzy c-means clustering and partitioning around medoids based on the following three criteria: average dissimilarity between subjects within clusters, average Dunn index and average silhouette width under four various covariate scenarios. Simulation studies show that the KMC algorithm has overall better performance compared with the other three clustering algorithms. Therefore, we recommend using the KMC algorithm to group similar subjects based on the transformed GPS. |
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Keywords: | fuzzy c-means clustering algorithm generalized propensity score k-means clustering algorithm model-based clustering algorithm observational studies partitioning around medoids algorithm |
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