We present motivation and new commands for modeling count data. While our focus is to present new commands for estimating count data, we also discuss generalized binomial regression and present the zero-inflated versions of each model. Copyright 2014 by StataCorp LP.

We present new commands for analyzing count-data regression models for truncated distributions. The trncregress command allows specification of a regression model for the mean of the truncated distribution through options. In addition to support for truncated Poisson and negative binomial,...

This note is motivated by recent works of Xie et al. (2009) and Xiang et al. (2007). Herein, we simplify the score statistic presented by Xie et al. (2009) to test overdispersion in the zero-inflated generalized Poisson (ZIGP) mixed model, and discuss an extension to test overdispersion...

TWe present new Stata commands for estimating several regression models suitable for analyzing overdispersed count outcomes. The nbregp command nests the dispersion(constant) and dispersion(mean) versions of Stata’s nbreg command in a model for negative binomial(p) regression. The zignbreg...

We present new Stata commands for carrying out several regression commands suitable for binomial outcomes. The zib command extends Stata’s binreg command to allow zero inflation. The betabin command fits binomial regression models allowing for beta overdispersion, and the zibbin command fits a...

We present new Stata commands for carrying out exact Wilcoxon one-sample and two-sample comparisons of the median. Nonparametric tests are often used in clinical trials, in which it is not uncommon to have small samples. In such situations, researchers are accustomed to making inferences by...

We present motivation and new Stata commands for modeling count data. While the focus of this article is on modeling data with underdispersion, the new command for fitting generalized Poisson regression models is also suitable as an alternative to negative binomial regression for overdispersed data.

Testing non-inferiority in active-controlled clinical trials examines whether a new procedure is, to a pre-specified amount, no worse than an existing procedure. To assess non-inferiority between two procedures using clustered matched-pair binary data, two new statistical tests are...

In diagnostic methods evaluation, analysts commonly focus on the relative size of the treatment difference (ratio of marginal probabilities) between a new and an existing procedures. To assess non-inferiority (a new procedure is, to a pre-specified amount, no worse than an existing procedure)...