Cure models can be used to simultaneously estimate the proportion of cancer patients who are eventually cured of their disease and the survival of those who remain "uncured". One limitation of parametric cure models is that the functional form of the survival of the "uncured" has to be...

Healthcare cost-effectiveness analysis (CEA) often uses individual patient data (IPD) from multinational randomised controlled trials. Although designed to account for between-patient sampling variability in the clinical and economic data, standard analytical approaches to CEA ignore the...

Competing risks occur in survival analysis when a subject is at risk of more than one type of event. A classic example is when there is consideration of different causes of death. Interest may lie in the cause-specific hazard rates, which can be estimated using standard survival techniques by...

It is usual in time-to-event data to have more than one event of interest, for example, time to death from different causes. Competing risks models can be applied in these situations where events are considered mutually exclusive absorbing states. That is, we have some initial state—for...

We present the Stata package stgenreg for the parametric analysis of survival data. Any user-defined hazard or log hazard function can be specified, with the model estimated using maximum likelihood utilizing numerical quadrature. Standard parametric models (for example, the Weibull proportional...

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...

In population-based cancer studies, cure is said to occur when the mortality (hazard) rate in the diseased group of individuals returns to the same level as that expected in the general population. The cure fraction (the proportion of patients cured of disease) is of interest to patients and a...

There is an increasing need to establish whether health-care interventions are cost effective as well as clinically effective. It is becoming increasingly common for cost studies to be incorporated into clinical trials, either on all patients or more usually on a subset of patients. Establishing...

This article focuses on the modelling and prediction of costs due to disease accrued over time, to inform the planning of future services and budgets. It is well documented that the modelling of cost data is often problematic due to the distribution of such data; for example, strongly right...

The paper demonstrates how cost-effectiveness decision analysis may be implemented from a Bayesian perspective, using Markov chain Monte Carlo simulation methods for both the synthesis of relevant evidence input into the model and the evaluation of the model itself. The desirable aspects of a...