Abstract
Extrapolations of parametric survival models fitted to censored data are routinely used in the assessment of health technologies to estimate mean survival, particularly in diseases that potentially reduce the life expectancy of patients. Akaike’s information criterion (AIC) and Bayesian information criterion (BIC) are commonly used in health technology assessment alongside an assessment of plausibility to determine which statistical model best fits the data and should be used for prediction of long-term treatment effects. We compare fit and estimates of restricted mean survival time (RMST) from 8 parametric models and contrast models preferred in terms of AIC, BIC, and log-likelihood, without considering model plausibility. We assess the methods’ suitability for selecting a parametric model through simulation of data replicating the follow-up of intervention arms for various time-to-event outcomes from 4 clinical trials. Follow-up was replicated through the consideration of recruitment duration and minimum and maximum follow-up times. Ten thousand simulations of each scenario were performed. We demonstrate that the different methods can result in disagreement over the best model and that it is inappropriate to base model selection solely on goodness-of-fit statistics without consideration of hazard behavior and plausibility of extrapolations. We show that typical trial follow-up can be unsuitable for extrapolation, resulting in unreliable estimation of multiple parameter models, and infer that selecting survival models based only on goodness-of-fit statistics is unsuitable due to the high level of uncertainty in a cost-effectiveness analysis. This article demonstrates the potential problems of overreliance on goodness-of-fit statistics when selecting a model for extrapolation. When follow-up is more mature, BIC appears superior to the other selection methods, selecting models with the most accurate and least biased estimates of RMST.
Highlights
BackgroundIn England and Wales, when a medical device or pharmacological developer wishes to get a technology approved for a new indication, they submit a report containing clinical and cost-effectiveness evidence concluding that their technology will offer cost-effective benefit to the National Health Service (NHS)
We have only considered RMST from single arms of trials
This study demonstrates issues with parametric extrapolation of time-to-event data and the reliance on AIC and BIC to select a preferred model
Summary
In England and Wales, when a medical device or pharmacological developer wishes to get a technology approved for a new indication, they submit a report containing clinical and cost-effectiveness evidence concluding that their technology will offer cost-effective benefit to the National Health Service (NHS). The National Institute of Health and Care Excellence (NICE) is responsible for deciding which treatments should be reimbursed by the NHS. Many have their clinical and cost-effectiveness assessed through the single technology appraisal (STA) process, where evidence submitted by a pharmaceutical company is appraised by an independent evidence review group prior to a discussion and decision by a NICE technology appraisal committee. If the most plausible ICER falls below certain thresholds, the treatment would usually be considered cost-effective and be recommended for reimbursement
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Medical decision making : an international journal of the Society for Medical Decision Making
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
