The Cox proportional hazards model has been used extensively in medicine over the last 40 years. A popular application is to develop a multivariable prediction model, often a prognostic model to predict the clinical outcome of patients with a particular disorder from "baseline" factors measured...

Royston (2014, Stata Journal 14: 738–755) explained how a popular application of the Cox proportional hazards model "is to develop a multivariable prediction model, often a prognostic model to predict the future clinical outcome of patients with a particular disorder from 'baseline' factors...

I provide a new programming tool, cmpute, to manage conveniently the creation of a new variable or the replacement of an existing variable interactively or within a Stata program. Copyright 2013 by StataCorp LP.

We consider how to represent sigmoid-type regression relationships in a practical and parsimonious way. A pure sigmoid relationship has an asymptote at both ends of the range of a continuous covariate. Curves with a single asymptote are also important in practice. Many smoothers, such as...

In an era in which doctors and patients aspire to personalized medicine and more sophisticated risk estimation, detecting and modeling interactions between covariates or between covariates and treatment is increasingly important. In observational studies (for example, in epidemiology),...

There is increasing interest in the medical world in the possibility of tailoring treatment to the individual patient. Statistically, the relevant task is to identify interactions between covariates and treatments, such that the patient’s value of a given covariate influences how strongly (or...

Royston and Parmar (2002, Statistics in Medicine 21: 2175 – 2197) developed a class of flexible parametric survival models that were programmed in Stata with the stpm command (Royston, 2001, Stata Journal 1:1-28). In this article, we introduce a new command, stpm2, that extends the...

This tutorial aims to cover many of the aspects of the flexible parametric alternatives to the Cox model that we have been developing.

We present an update of mim, a program for managing multiply im- puted datasets and performing inference (estimating parameters) using Rubin’s rules for combining estimates from imputed datasets. The new features of particular importance are an option for estimating the Monte Carlo error (due...