This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 296
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #308549
Title: Propensity Score Matching in Randomized Clinical Trials
Author(s): Zhenzhen Xu*+ and John David Kalbfleisch
Companies: University of Michigan and University of Michigan
Address: Department of Biostatistics, Ann Arbor, MI, 48109-2029,
Keywords: Cluster randomized trials ; Experimental design ; Optimal full matching ; Propensity score matching ; Randomization test ; Optimal tripartite matching

Cluster randomization trials with few clusters have been widely used in recent years for evaluation of health care strategies. On average, randomization achieves balance in both known and unknown confounding factors. In practice, however, investigators can only introduce a small amount of stratification and cannot balance on all the variables simultaneously. A new design, the balance match weighted(BMW) design, which applies the optimal matching with constraints technique to a prospective randomized design, was introduced (Biometrics 2009). In this paper, we generalize the BMW design to other major areas of applications. One example is clinical trials with three arms where the generalized design would apply an optimal tripartite matching technique to randomized samples. BMW Design-based randomization tests are also developed and their performance investigated under various setting.

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