Conditional analysis of mixed Poisson processes with baseline counts: implications for trial design and analysis.

Richard J Cook; Wei Wei

doi:10.1093/biostatistics/4.3.479

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Conditional analysis of mixed Poisson processes with baseline counts: implications for trial design and analysis.

Richard J Cook·Wei Wei

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Abstract

The design of clinical trials is typically based on marginal comparisons of a primary response under two or more treatments. The considerable gains in efficiency afforded by models conditional on one or more baseline responses has been extensively studied for Gaussian models. The purpose of this article is to present methods for the design and analysis of clinical trials in which the response is a count or a point process, and a corresponding baseline count is available prior to randomization. The methods are based on a conditional negative binomial model for the response given the baseline count and can be used to examine the effect of introducing selection criteria on power and sample size requirements. We show that designs based on this approach are more efficient than those proposed by McMahon et al. (1994).

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Algorithms Clinical Trials as Topic Data Collection Data Interpretation, Statistical Epilepsy Humans Models, Statistical Poisson Distribution Research Design Sample Size Comparative Study Journal Article Research Support, Non-U.S. Gov't

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Richard J Cook, Wei Wei. 2003. Conditional analysis of mixed Poisson processes with baseline counts: implications for trial design and analysis.. https://doi.org/10.1093/biostatistics%2F4.3.479

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