Gallacher, Daniel, Kimani, Peter and Stallard, Nigel (2021) Extrapolating parametric survival models in Health Technology Assessment using model averaging : a simulation study. Medical Decision Making, 41 (4). pp. 476-484. doi:10.1177/0272989x21992297 ISSN 0272-989X.

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Abstract

Previous work examined the suitability of relying on routine methods of model selection when extrapolating survival data in a health technology appraisal setting. Here we explore solutions to improve reliability of restricted mean survival time (RMST) estimates from trial data by assessing model plausibility and implementing model averaging. We compare our previous methods of selecting a model for extrapolation using the Akaike information criterion (AIC) and Bayesian information criterion (BIC). Our methods of model averaging include using equal weighting across models falling within established threshold ranges for AIC and BIC and using BIC-based weighted averages. We apply our plausibility assessment and implement model averaging to the output of our previous simulations, where 10,000 runs of 12 trial-based scenarios were examined. We demonstrate that removing implausible models from consideration reduces the mean squared error associated with the restricted mean survival time (RMST) estimate from each selection method and increases the percentage of RMST estimates that were within 10% of the RMST from the parameters of the sampling distribution. The methods of averaging were superior to selecting a single optimal extrapolation, aside from some of the exponential scenarios where BIC already selected the exponential model. The averaging methods with wide criterion-based thresholds outperformed BIC-weighted averaging in the majority of scenarios. We conclude that model averaging approaches should feature more widely in the appraisal of health technologies where extrapolation is influential and considerable uncertainty is present. Where data demonstrate complicated underlying hazard rates, funders should account for the additional uncertainty associated with these extrapolations in their decision making. Extended follow-up from trials should be encouraged and used to review prices of therapies to ensure a fair price is paid.

Item Type:	Journal Article
Subjects:	R Medicine > RA Public aspects of medicine R Medicine > RC Internal medicine T Technology > T Technology (General)
Divisions:	Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School
SWORD Depositor:	Library Publications Router
Library of Congress Subject Headings (LCSH):	Medical technology -- Evaluation -- Simulation methods, Technology Assessment, Medical innovations -- Cost effectiveness -- Mathematical models, Cancer -- Treatment -- Technological innovations
Journal or Publication Title:	Medical Decision Making
Publisher:	Sage Publications, Inc.
ISSN:	0272-989X
Official Date:	25 February 2021
Dates:	Date Event 25 February 2021 Published 12 January 2021 Accepted
Volume:	41
Number:	4
Page Range:	pp. 476-484
DOI:	10.1177/0272989x21992297
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
Date of first compliant deposit:	13 May 2021
Date of first compliant Open Access:	13 May 2021
Related URLs:	Related item in WRAP

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