We describe Stata macros that implement the composite link approach to missing data in log-linear models first described by David Rindskopf (Psychometrika, 1992, V57, 29-42). When a missing value occurs among the variables that form a contingency table, the resulting observation contributes to...

This contribution is based on my programs bspline and frencurv, which are used to generate bases for Schoenberg B-splines and splines parameterized by their values at reference points on the X-axis (presented in STB-57 as insert sg151). The program frencurv ("French curve") makes it possible for...

This presentation discusses how the tasks involved with carrying out a sizable research project, involving panel data at both monthly and daily frequencies, could be efficiently managed by making use of built-in and user-contributed features of Stata. The project entails the construction of a...

Nonparametric estimates of hazard rates can be computed as functions of time (e.g., age or calendar time). Given random variations in survival times, estimates of the hazard typically must be smoothed to distinguish trends from noise. Left truncation (at a known age or time) and right censoring...

A Stata routine for estimating the cumulative incidence rate (CIR) and its standard error in the presence of competing risks will be demonstrated. The program mtable will have the same features as ltable command in Stata. In addition to the CIR estimates, the program will have an option to...

There are a number of methods of analyzing data that consists of several distinct categories, with the categories ordered in some manner. Analysis of such data is commonly based on a generalized linear model of the cumulative response probability, either the cumulative odds model (ologit) or 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...

In the standard survival model, the risk of failure is non-zero for all cases. A split-population (or cure) survival model relaxes this assumption and allows an (estimable) fraction of cases never to experience the event. This presentation reports on an implementation of a discrete time (or...

gllamm is a program to fit generalised linear latent and mixed models. Since gllamm6 appeared in the STB (sg129), a large number of new features have been added. Two important extensions will be discussed: 1) More response processes can now be modelled including ordered and unordered categorical...