This paper presents a command, glst, for trend estimation across different exposure levels for either single or multiple summarized case-control, incidence-rate, and cumulative incidence data. This approach is based on constructing an approximate covariance estimate for the log relative risks...

The Stata command xtreg estimates the random-effects linear regression model, for which the random effects are assumed to be normally distributed with zero mean and non-negative variance, s^2_{i,t}. Testing homogeneity across units is equivalent to testing the null hypothesis H_0: s^2_{i,t} = 0,...

We present the postestimation command xtvc to provide confidence intervals for the variance components of random-effects linear regression models. This command must be used after xtreg with option mle. Confidence intervals are based on the inversion of a score-based test (Bottai 2003). Copyright...

We present the new laplace command for estimating Laplace regression, which models quantiles of a possibly censored outcome variable given covariates. We illustrate laplace with an example from a clinical trial on survival in patients with metastatic renal carcinoma. We also report the results...

We present a set of Stata commands for the estimation, prediction, and graphical representation of logistic quantile regression described by Bottai, Cai, and McKeown (2010, Statistics in Medicine 29: 309–317). Logistic quantile regression models the quantiles of outcome variables that take on...

A common aim of epidemiological research is to assess the association between a particular exposure and a particular outcome, controlling for a set of additional covariates. This is often done by using a regression model for the outcome, conditional on exposure and covariates. A commonly used...

The use of flexible models for the relationship between a quantitative covariate and the response variable can be limited by the difficulty in interpret- ing the regression coefficients. In this article, we present a new postestimation command, xblc, that facilitates tabular and graphical...

We examine the asymptotic behaviour of confidence regions in identifiable one-dimensional parametric models with smooth likelihood function and information equal to zero at a critical point of the parameter space. Confidence regions are based on inversion of the likelihood ratio test statistic...

Classification errors, selection bias, and uncontrolled confounding are likely to be present in most epidemiological studies, but the uncertainty introduced by this type of biases is seldom quantified. The authors present a simple yet easy-to-use method to adjust the relative risk of a disease...