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,...

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...

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)...

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.

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 the empirical analysis of information asymmetry in automobile insurance markets, prior research used a dichotomous measurement approach that induces excessive bundling in coverage measurements and sample selection biases. To improve on the conditional correlation method for testing...

Generalized linear models (GLMs) extend linear regression to models with a non-Gaussian, or even discrete, response. GLM theory is predicated on the exponential family of distributions—a class so rich that it includes the commonly used logit, probit, and Poisson distributions. Although one can...