Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study

Abstract Background Estimates of future survival can be a key evidence source when deciding if a medical treatment should be funded. Current practice is to use standard parametric models for generating extrapolations. Several emerging, more flexible, survival models are available which can provide i...

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Autores principales:	Benjamin Kearns, Matt D. Stevenson, Kostas Triantafyllopoulos, Andrea Manca
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Survival analysis Forecasting Extrapolation Medicine (General) R5-920
Acceso en línea:	https://doaj.org/article/5f080a74199b415888a42aab32fc2d1a
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spelling	oai:doaj.org-article:5f080a74199b415888a42aab32fc2d1a2021-11-28T12:38:45ZComparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study10.1186/s12874-021-01460-11471-2288https://doaj.org/article/5f080a74199b415888a42aab32fc2d1a2021-11-01T00:00:00Zhttps://doi.org/10.1186/s12874-021-01460-1https://doaj.org/toc/1471-2288Abstract Background Estimates of future survival can be a key evidence source when deciding if a medical treatment should be funded. Current practice is to use standard parametric models for generating extrapolations. Several emerging, more flexible, survival models are available which can provide improved within-sample fit. This study aimed to assess if these emerging practice models also provided improved extrapolations. Methods Both a simulation study and a case-study were used to assess the goodness of fit of five classes of survival model. These were: current practice models, Royston Parmar models (RPMs), Fractional polynomials (FPs), Generalised additive models (GAMs), and Dynamic survival models (DSMs). The simulation study used a mixture-Weibull model as the data-generating mechanism with varying lengths of follow-up and sample sizes. The case-study was long-term follow-up of a prostate cancer trial. For both studies, models were fit to an early data-cut of the data, and extrapolations compared to the known long-term follow-up. Results The emerging practice models provided better within-sample fit than current practice models. For data-rich simulation scenarios (large sample sizes or long follow-up), the GAMs and DSMs provided improved extrapolations compared with current practice. Extrapolations from FPs were always very poor whilst those from RPMs were similar to current practice. With short follow-up all the models struggled to provide useful extrapolations. In the case-study all the models provided very similar estimates, but extrapolations were all poor as no model was able to capture a turning-point during the extrapolated period. Conclusions Good within-sample fit does not guarantee good extrapolation performance. Both GAMs and DSMs may be considered as candidate extrapolation models in addition to current practice. Further research into when these flexible models are most useful, and the role of external evidence to improve extrapolations is required.Benjamin KearnsMatt D. StevensonKostas TriantafyllopoulosAndrea MancaBMCarticleSurvival analysisForecastingExtrapolationMedicine (General)R5-920ENBMC Medical Research Methodology, Vol 21, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Survival analysis Forecasting Extrapolation Medicine (General) R5-920
spellingShingle	Survival analysis Forecasting Extrapolation Medicine (General) R5-920 Benjamin Kearns Matt D. Stevenson Kostas Triantafyllopoulos Andrea Manca Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study
description	Abstract Background Estimates of future survival can be a key evidence source when deciding if a medical treatment should be funded. Current practice is to use standard parametric models for generating extrapolations. Several emerging, more flexible, survival models are available which can provide improved within-sample fit. This study aimed to assess if these emerging practice models also provided improved extrapolations. Methods Both a simulation study and a case-study were used to assess the goodness of fit of five classes of survival model. These were: current practice models, Royston Parmar models (RPMs), Fractional polynomials (FPs), Generalised additive models (GAMs), and Dynamic survival models (DSMs). The simulation study used a mixture-Weibull model as the data-generating mechanism with varying lengths of follow-up and sample sizes. The case-study was long-term follow-up of a prostate cancer trial. For both studies, models were fit to an early data-cut of the data, and extrapolations compared to the known long-term follow-up. Results The emerging practice models provided better within-sample fit than current practice models. For data-rich simulation scenarios (large sample sizes or long follow-up), the GAMs and DSMs provided improved extrapolations compared with current practice. Extrapolations from FPs were always very poor whilst those from RPMs were similar to current practice. With short follow-up all the models struggled to provide useful extrapolations. In the case-study all the models provided very similar estimates, but extrapolations were all poor as no model was able to capture a turning-point during the extrapolated period. Conclusions Good within-sample fit does not guarantee good extrapolation performance. Both GAMs and DSMs may be considered as candidate extrapolation models in addition to current practice. Further research into when these flexible models are most useful, and the role of external evidence to improve extrapolations is required.
format	article
author	Benjamin Kearns Matt D. Stevenson Kostas Triantafyllopoulos Andrea Manca
author_facet	Benjamin Kearns Matt D. Stevenson Kostas Triantafyllopoulos Andrea Manca
author_sort	Benjamin Kearns
title	Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study
title_short	Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study
title_full	Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study
title_fullStr	Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study
title_full_unstemmed	Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study
title_sort	comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study
publisher	BMC
publishDate	2021
url	https://doaj.org/article/5f080a74199b415888a42aab32fc2d1a
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Comparing current and emerging practice models for the extrapolation of survival data: a simulation study and case-study

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