Bayesian Solutions for Handling Uncertainty in Survival Extrapolation

Hernández, Miguel Ángel Negrín; Nam, Julian; Briggs, Andrew

doi:10.1177/0272989x16650669

Cited by 17 publications

(18 citation statements)

References 34 publications

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“…When the modeler has access to the original data, the standard approach is to fit a variety of parametric models to the data, and, using a goodness-of-fit statistic such as the Akaike and/or Bayesian information criterion, choose the best-fitting model to extrapolate beyond the original data, even though goodness-of-fit to the observed data is not an appropriate test of the fitted model’s ability to extrapolate accurately. Negrin et al [ 34 ] and Latimer [ 16 ] suggest conducting sensitivity analyses by comparing, say, the cost-effectiveness results for the best-fitting model with results based on those that fit less well. This approach shows whether an intervention deemed cost-effective (or not) using the best-fitting model remains so when other models are used and focuses attention on the effects of the extrapolation method on decision uncertainty.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

“…This approach shows whether an intervention deemed cost-effective (or not) using the best-fitting model remains so when other models are used and focuses attention on the effects of the extrapolation method on decision uncertainty. Another approach is to fit parametric models to different subperiods within the observed data to explore the stability of the estimated parameters and, rather than using the single best-fitting model, use Bayesian model averaging to combine models for extrapolation [ 34 ]. Modelers who are limited to the published data will not be able to use these approaches, but should consider widening the range around extrapolated probabilities to reflect the additional uncertainty associated with extrapolation.…”

Section: Sensitivity Analysesmentioning

confidence: 99%

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Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers

Gidwani

Russell

2020

PharmacoEconomics

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This tutorial presents practical guidance on transforming various types of information published in journals, or available online from government and other sources, into transition probabilities for use in state-transition models, including cost-effectiveness models. Much, but not all, of the guidance has been previously published in peer-reviewed journals. Our purpose is to collect it in one location to serve as a stand-alone resource for decision modelers who draw most or all of their information from the published literature. Our focus is on the technical aspects of manipulating data to derive transition probabilities. We explain how to derive model transition probabilities from the following types of statistics: relative risks, odds, odds ratios, and rates. We then review the well-known approach for converting probabilities to match the model’s cycle length when there are two health-state transitions and how to handle the case of three or more health-state transitions, for which the two-state approach is not appropriate. Other topics discussed include transition probabilities for population subgroups, issues to keep in mind when using data from different sources in the derivation process, and sensitivity analyses, including the use of sensitivity analysis to allocate analyst effort in refining transition probabilities and ways to handle sources of uncertainty that are not routinely formalized in models. The paper concludes with recommendations to help modelers make the best use of the published literature.

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Section: Sensitivity Analysesmentioning

confidence: 99%

Section: Sensitivity Analysesmentioning

confidence: 99%

Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers

Gidwani

Russell

2020

PharmacoEconomics

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“…To tackle such problems, we adopt the methodology of Bayesian learning of models, which in theory provides reliable estimation of uncertainty intervals [15,16,17]. This approach, however, requires intensive computations, as discussed in [18,19].…”

Section: Introductionmentioning

confidence: 99%

Bayesian averaging over decision tree models: An application for estimating uncertainty in trauma severity scoring

Schetinin

Jakaite

Krzanowski

2018

International Journal of Medical Informatics

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“…However, there is no guarantee that any model, even the best-fit model, will give accurate predictions of the future. 2,6 Alternative approaches to survival curve extrapolation include model-averaging techniques, [7][8][9] hybrid models, 10,11 Bayesian poly-Weibull models, 12,13 cure models, 14 and combination of trial and external data. 15,16 The need for long-term data to perform survival curve extrapolation has been expressed.…”

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confidence: 99%

An Evaluation of Survival Curve Extrapolation Techniques Using Long-Term Observational Cancer Data

Vickers¹

2019

Med Decis Making

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Objectives. Uncertainty in survival prediction beyond trial follow-up is highly influential in cost-effectiveness analyses of oncology products. This research provides an empirical evaluation of the accuracy of alternative methods and recommendations for their implementation. Methods. Mature (15-year) survival data were reconstructed from a published database study for “no treatment,” radiotherapy, surgery plus radiotherapy, and surgery in early stage non–small cell lung cancer in an elderly patient population. Censored data sets were created from these data to simulate immature trial data (for 1- to 10-year follow-up). A second data set with mature (9-year) survival data for no treatment was used to extrapolate the predictions from models fitted to the first data set. Six methodological approaches were used to fit models to the simulated data and extrapolate beyond trial follow-up. Model performance was evaluated by comparing the relative difference in mean survival estimates and the absolute error in the difference in mean survival v. the control with those from the original mature survival data set. Results. Model performance depended on the treatment comparison scenario. All models performed reasonably well when there was a small short-term treatment effect, with the Bayesian model coping better with shorter follow-up times. However, in other scenarios, the most flexible Bayesian model that could be estimated in practice appeared to fit the data less well than the models that used the external data separately. Where there was a large treatment effect (hazard ratio = 0.4), models that used external data separately performed best. Conclusions. Models that directly use mature external data can improve the accuracy of survival predictions. Recommendations on modeling strategies are made for different treatment benefit scenarios.

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Bayesian Solutions for Handling Uncertainty in Survival Extrapolation

Cited by 17 publications

References 34 publications

Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers

Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers

Bayesian averaging over decision tree models: An application for estimating uncertainty in trauma severity scoring

An Evaluation of Survival Curve Extrapolation Techniques Using Long-Term Observational Cancer Data

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