Abstract
Health economic evaluations require estimates of expected survival from patients receiving different interventions, often over a lifetime. However, data on the patients of interest are typically only available for a much shorter follow‐up time, from randomised trials or cohorts. Previous work showed how to use general population mortality to improve extrapolations of the short‐term data, assuming a constant additive or multiplicative effect on the hazards for all‐cause mortality for study patients relative to the general population. A more plausible assumption may be a constant effect on the hazard for the specific cause of death targeted by the treatments. To address this problem, we use independent parametric survival models for cause‐specific mortality among the general population. Because causes of death are unobserved for the patients of interest, a polyhazard model is used to express their all‐cause mortality as a sum of latent cause‐specific hazards. Assuming proportional cause‐specific hazards between the general and study populations then allows us to extrapolate mortality of the patients of interest to the long term. A Bayesian framework is used to jointly model all sources of data. By simulation, we show that ignoring cause‐specific hazards leads to biased estimates of mean survival when the proportion of deaths due to the cause of interest changes through time. The methods are applied to an evaluation of implantable cardioverter defibrillators for the prevention of sudden cardiac death among patients with cardiac arrhythmia. After accounting for cause‐specific mortality, substantial differences are seen in estimates of life years gained from implantable cardioverter defibrillators. © 2014 The Authors Statistics in Medicine Published by John Wiley & Sons Ltd.
Highlights
In health economic evaluations, we compare the expected cost and clinical effectiveness associated with different treatments or other health technologies
Extrapolations of survival of patients from randomised trials or study cohorts can often be facilitated with official survival statistics from the general population
This requires carefully characterising the differences between the population and the study patients
Summary
We compare the expected cost and clinical effectiveness associated with different treatments or other health technologies. In the UK, the organisation responsible for these appraisals is the National Institute for Health and Clinical Excellence, which recommends whether a new technology should be given public funding in the presence of a limited budget. An important component of many health economic evaluations is the effect of the new treatment on the long-term survival or some functional of it, such as mean survival or life expectancy for the groups of interest. Treatment effects are usually obtained from RCTs. for many chronic diseases, the choice of treatment has an impact on survival over much longer periods than those covered by the follow-up of trials. Estimates of expected lifetime survival are required to evaluate each treatment policy, but only a short-term survival curve is available from the trial
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