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),...

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

All doctors treating patients with Breast Cancer know which key variables indicate a good prognosis and which values decrease the chances of surviving. However because of complex interactions between the variables and survival doctors cannot give an individualized prognosis to a patient. The...

Cox proportional-hazard regression has been essentially the automatic choice of analysis tool for modeling survival data in medical studies. However, the Cox model has several intrinsic features that may cause problems for the analyst or an interpreter of the data. These include the necessity of...

There has been a considerable growth of interest among Stata users and more widely in the practical use of multiple imputation as a principled route to the analysis of datasets with missing covariate values. Sophisticated Stata software (ice) is available for creating multiply imputed datasets....

We consider modelling and testing for `interaction' between a continuous covariate X and a categorical covariate C in a regression model. Here C represents two treatment arms in a parallel-group clinical trial and X is a prognostic factor which may influence response to treatment. Usually X is...

The existing Stata command bstrap takes a user-defined program and calculates normal approximation, percentile and bias- corrected percentile bootstrap confidence intervals. However, these intervals are not the most accurate available. In this article, we describe a new command, bci, which...

We consider modelling and testing for `interaction' between a continuous covariate X and a categorical covariate C in a regression model. Here C represents two treatment arms in a parallel-group clinical trial and X is a prognostic factor which may influence response to treatment. Usually X is...

Regression models play a central role in epidemiology and clinical studies. In epidemiology the emphasis is typically either on determining whether a given risk factor affects the outcome of interest (adjusted for confounders), or on estimating a dose/response curve for a given factor, again...