Comparison of clustering algorithms on generalized propensity score in observational studies: a simulation study

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....

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Bibliographic Details
Published in:Journal of statistical computation and simulation Vol. 83; no. 12; pp. 2206 - 2218
Main Authors: Tu, Chunhao, Jiao, Shuo, Koh, Woon Yuen
Format: Journal Article
Language:English
Published: Abingdon Taylor & Francis 01.12.2013
Taylor & Francis Ltd
Subjects:
Algorithms
Cluster analysis
Clustering
Clusters
Comparative analysis
Computer simulation
Fuzzy
fuzzy c-means clustering algorithm
Fuzzy logic
Fuzzy set theory
generalized propensity score
k-means clustering algorithm
model-based clustering algorithm
observational studies
partitioning around medoids algorithm
Performance indices
ISSN:0094-9655, 1563-5163
Online Access:Get full text
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Summary: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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ISSN:0094-9655
1563-5163
DOI:10.1080/00949655.2012.685169