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Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study

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  • Daniel Gallacher

    (Warwick Medical School, University of Warwick, Coventry, Warwickshire, UK)

  • Peter Kimani

    (Warwick Medical School, University of Warwick, Coventry, Warwickshire, UK)

  • Nigel Stallard

    (Warwick Medical School, University of Warwick, Coventry, Warwickshire, UK)

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.

Suggested Citation

  • Daniel Gallacher & Peter Kimani & Nigel Stallard, 2021. "Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study," Medical Decision Making, , vol. 41(1), pages 37-50, January.
    • Handle: RePEc:sae:medema:v:41:y:2021:i:1:p:37-50
    DOI: 10.1177/0272989X20973201
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    References listed on IDEAS

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    1. Helen Bell Gorrod & Ben Kearns & John Stevens & Praveen Thokala & Alexander Labeit & Nicholas Latimer & David Tyas & Ahmed Sowdani, 2019. "A Review of Survival Analysis Methods Used in NICE Technology Appraisals of Cancer Treatments: Consistency, Limitations, and Areas for Improvement," Medical Decision Making, , vol. 39(8), pages 899-909, November.
    2. Adrian Vickers, 2019. "An Evaluation of Survival Curve Extrapolation Techniques Using Long-Term Observational Cancer Data," Medical Decision Making, , vol. 39(8), pages 926-938, November.
    3. Jackson, Christopher, 2016. "flexsurv: A Platform for Parametric Survival Modeling in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 70(i08).
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    2. Lin-Yen Wang & Tsair-Wei Chien & Willy Chou, 2021. "Using the IPcase Index with Inflection Points and the Corresponding Case Numbers to Identify the Impact Hit by COVID-19 in China: An Observation Study," IJERPH, MDPI, vol. 18(4), pages 1-16, February.
    3. Taihang Shao & Mingye Zhao & Leyi Liang & Lizheng Shi & Wenxi Tang, 2023. "Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067," PharmacoEconomics - Open, Springer, vol. 7(3), pages 383-392, May.

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