This paper discusses and illustrates the method of regression calibration. This is a straightforward technique for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003)....

We discuss and illustrate the method of simulation extrapolation for fitting models with additive measurement error. We present this discussion in terms of generalized linear models (GLMs) following the notation defined in Hardin and Carroll (2003). As in Hardin, Schmiediche, and Carroll (2003),...

This paper introduces additive measurement error in a generalized linear-model context. We discuss the types of measurement error along with their effects on fitted models. In addition, we present the notational conventions to be used in this and the accompanying papers. Copyright 2003 by...

This paper discusses and illustrates the qvf command for fitting generalized linear models. The differences between this new command and StataÕs glm command are highlighted. One of the most notable features of the qvf command is its ability to include instrumental variables. This functionality...

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,...

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.