Abstract #300661

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JSM 2003 Abstract #300661
Activity Number: 452
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Biopharmaceutical Section
Abstract - #300661
Title: Analysis of Overdispersed Count Data in Clinical Trials
Author(s): Simon L. Davies*+ and Mani Y. Lakshminarayanan
Companies: Johnson & Johnson and Centocor, Inc.
Address: 1666 Callowhill St., Philadelphia, PA, 19130-4117,
Keywords: Poisson counts ; Zero-Inflated Poisson models ; negative binomial model ; EM algorithm ; likelihood ratio ; sample size
Abstract:

One of the initial and fundamental questions, which must be resolved in designing clinical trials, is what to utilize as the primary endpoint. There are many endpoints in clinical trials based on count data. Due to analytical difficulties, such endpoints are often reformulated as either continuous or discrete or time-to-event endpoints. Examples of count data include the number of new enhancing lesions read on MRI scans in multiple sclerosis trials (NEJM 2003; 348:15-23) and the number of seizures in epilepsy trials (NEJM 2001; 345:311-318). Poisson regression models provide an established framework for dealing with count data. However, in clinical trial applications, many of the endpoints based on count data may suffer from excess zeros, which consequently leads to overdispersion. Overdispersed count data are primarily modeled based on a negative binomial model or a Zero-Inflated Poisson (ZIP) model (1992). Using clinical trial endpoints involving counts, we explore and compare a selection of models currently empployed in practice to handle overdispersion. Additionally, we examine analyses issues such as covariates and sample size.

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