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

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  • Adrian Vickers

Abstract

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.

Suggested Citation

  • 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.
  • Handle: RePEc:sae:medema:v:39:y:2019:i:8:p:926-938
    DOI: 10.1177/0272989X19875950
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    References listed on IDEAS

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    1. Paul B Conn & Devin S Johnson & Peter L Boveng, 2015. "On Extrapolating Past the Range of Observed Data When Making Statistical Predictions in Ecology," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-16, October.
    2. Christopher Jackson & John Stevens & Shijie Ren & Nick Latimer & Laura Bojke & Andrea Manca & Linda Sharples, 2017. "Extrapolating Survival from Randomized Trials Using External Data: A Review of Methods," Medical Decision Making, , vol. 37(4), pages 377-390, May.
    3. Lois G. Kim & Simon G. Thompson, 2010. "Uncertainty and validation of health economic decision models," Health Economics, John Wiley & Sons, Ltd., vol. 19(1), pages 43-55, January.
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    1. 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.
    2. M. A. Chaudhary & M. Edmondson-Jones & G. Baio & E. Mackay & J. R. Penrod & D. J. Sharpe & G. Yates & S. Rafiq & K. Johannesen & M. K. Siddiqui & J. Vanderpuye-Orgle & A. Briggs, 2023. "Use of Advanced Flexible Modeling Approaches for Survival Extrapolation from Early Follow-up Data in two Nivolumab Trials in Advanced NSCLC with Extended Follow-up," Medical Decision Making, , vol. 43(1), pages 91-109, January.
    3. Zhaojing Che & Nathan Green & Gianluca Baio, 2023. "Blended Survival Curves: A New Approach to Extrapolation for Time-to-Event Outcomes from Clinical Trials in Health Technology Assessment," Medical Decision Making, , vol. 43(3), pages 299-310, April.

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