ArticleMedical decision making : an international journal of the Society for Medical Decision Making2021
Extrapolating Parametric Survival Models in Health Technology Assessment: A Simulation Study.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 44 citations in OpenAlex.
- Relative Survival Modeling for Appraising the Cost-Effectiveness of Life-Extending Treatments: An Application to Tafamidis for the Treatment of Transthyretin Amyloidosis with Cardiomyopathy.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Impact of Limited Sample Size and Follow-up on Partitioned Survival and Multistate Modeling-Based Health Economic Models: A Simulation Study.Medical decision making : an international journal of the Society for Medical Decision Making · 2025Article
- Matching plus regression adjustment for the estimation of the average treatment effect on survival outcomes: a case study with mosunetuzumab in relapsed/refractory follicular lymphoma.BMC medical research methodology · 2025Article
- SurvdigitizeR: an algorithm for automated survival curve digitization.BMC medical research methodology · 2024Article
- SurvInt: a simple tool to obtain precise parametric survival extrapolations.BMC medical informatics and decision making · 2024Article
- Survival Extrapolation Incorporating General Population Mortality Using Excess Hazard and Cure Models: A Tutorial.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Review
- Impact of Extrapolation Model Choices on the Structural Uncertainty in Economic Evaluations for Cancer Immunotherapy: A Case Study of Checkmate 067.PharmacoEconomics - open · 2023Article
- Comparing the cost-effectiveness of sintilimab + pemetrexed plus platinum and pemetrexed plus platinum alone as a first-line therapy for Chinese patients with nonsquamous non-small cell lung cancer.Translational cancer research · 2023Article
- A proposal to analyze the progression of non-dialytic chronic kidney disease by surrogate endpoints: introducing parametric survival models.Frontiers in medicine · 2023Article
- Article
- An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial.Medical decision making : an international journal of the Society for Medical Decision Making · 2022Article
- A progressive three-state model to estimate time to cancer: a likelihood-based approach.BMC medical research methodology · 2022Article
- Challenges of modelling approaches for network meta-analysis of time-to-event outcomes in the presence of non-proportional hazards to aid decision making: Application to a melanoma network.Statistical methods in medical research · 2022Article
- Impact of limited sample size and follow-up on single event survival extrapolation for health technology assessment: a simulation study.BMC medical research methodology · 2021Article
- Using the absolute advantage coefficient (AAC) to measure the strength of damage hit by COVID-19 in India on a growth-share matrix.European journal of medical research · 2021Article
- 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.International journal of environmental research and public health · 2021Observational
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.