title: Sample size calculation for comparing two negative binomial rates.
abstract: Negative binomial model has been increasingly used to model the count data in recent clinical trials. It is frequently chosen over Poisson model in cases of overdispersed count data that are commonly seen in clinical trials. One of the challenges of applying negative binomial model in clinical trial design is the sample size estimation. In practice, simulation methods have been frequently used for sample size estimation. In this paper, an explicit formula is developed to calculate sample size based on the negative binomial model. Depending on different approaches to estimate the variance under null hypothesis, three variations of the sample size formula are proposed and discussed. Important characteristics of the formula include its accuracy and its ability to explicitly incorporate dispersion parameter and exposure time. The performance of the formula with each variation is assessed using simulations.
authors.affiliation: Forest Research Institute, Harborside Financial Center, Jersey City, NJ 07311, U.S.A.
authors.forename: Haiyuan
authors.initials: H
authors.lastname: Zhu
authors.forename: Hassan
authors.initials: H
authors.lastname: Lakkis
mesh_terms: D001249:Asthma / Q000188:drug therapy; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D018401:Sample Size; D012602:Scopolamine Derivatives / Q000627:therapeutic use; D000069447:Tiotropium Bromide
chemical_list: D012602:Scopolamine Derivatives; D000069447:Tiotropium Bromide
keywords: count data; negative binomial model; power analysis; sample size calculation