Sample Size Estimation for Negative Binomial Regression Models with Distinct Shape Parameters

Author(s): Yongming Qu, Junxiang Luo

Journal Name: Applied Clinical Research, Clinical Trials and Regulatory Affairs (Discontinued)
Continued as Applied Drug Research, Clinical Trials and Regulatory Affairs

Volume 3 , Issue 2 , 2016

Graphical Abstract:


Background: Negative bionmial regression is a common statistical model for analyzing count data. For example, hypoglycemic events occurred in clinical trials studying anti-diabetes therapies are often analyzed using negative binomial regression. Recently, methods have been developed for calculating statistical power and sample size needed for negative binomial regression with a common shape parameter across treatment groups. Real data examples suggest that the shape parameters are often distinct when the hypoglycemia event rates between two treatment groups are different. This article extended the existing method of negative binomial regression for distinct shape parameters.

Methods: Three new methods are proposed for sample size calculation based on estimating the variance under null hypothesis: (1). Using the true rate and shape parameter based on the reference group; (2) Using the true rates and shape parameters under the alternative hypothesis; (3). Using the true shape parameters under alternative hypothesis and maximum likelihood estmator for the rate under the null hypothesis.

Results: Simulations were performed for various mean and shape parameters based on previous publications and based on hypoglycemic events data from clinical trials in diabetes. Results show that Methods (2) and (3) provided satisfactory estimation of sample size in which the simulated statistical power approximated the desired statistical power. In each case, the analysed sample size based on Method 2 was not smaller than the sample size based on Method 1.

Conclusion: Two methods for estimating the statistical power using negative binomial regression with distinct shape parameters are proposed and simulations show that they had satistifactory performance.

Keywords: Hypoglycemia, overdispersion, statistical power.

Rights & PermissionsPrintExport Cite as

Article Details

Year: 2016
Page: [107 - 112]
Pages: 6
DOI: 10.2174/2213476X03666160511110952
Price: $25

Article Metrics

PDF: 13