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Biostatistics 3:179-193 (2002)
© 2002 Oxford University Press

Propensity score modeling strategies for the causal analysis of observational data

Katherine Huppler Hullsiek and Thomas A. Louis

Katherine Huppler Hullsiek. Division of Biostatistics, School of Public Health, University of Minnesota, 2221 University Avenue S.E., Minneapolis, MN 55414, USA kathy-h{at}ccbr.umn.edu
Thomas A. Louis. The RAND Corporation, Arlington, VA 22202

Propensity score methods are used to estimate a treatment effect with observational data. This paper considers the formation of propensity score subclasses by investigating different methods for determining subclass boundaries and the number of subclasses used. We compare several methods: balancing a summary of the observed information matrix and equal-frequency subclasses. Subclasses that balance the inverse variance of the treatment effect reduce the mean squared error of the estimates and maximize the number of usable subclasses.

Keywords: Bias reduction; Confounding; Observational data; Propensity score methods


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