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Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study

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  • Mathyn Vervaart

Abstract

The net value of reducing decision uncertainty through collecting additional data is quantified by the expected net benefit of sampling (ENBS). The ENBS is the difference between the population-level expected value of sample information (EVSI) for the data collection exercise and the costs associated with the data collection. ENBS calculations for studies that collect survival data are complicated by the need to take into account censoring, and this has limited the application of value-of-information analysis in this setting. In this tutorial article, we present a general-purpose algorithm for calculating the ENBS for a study that collects survival data along with a step-by-step implementation in R. The algorithm is based on recently published methods for simulating survival data and computing EVSI that do not rely on the survival data being from a distribution with any particular parametric form and that can take into account any arbitrary censoring process. We illustrate the method using a real-life case study drawn from an appraisal of pembrolizumab plus axitinib for treating advanced renal cell carcinoma in which the initial decision was informed by immature survival data. Highlights The net value of reducing decision uncertainty by collecting additional data is quantified by the expected net benefit of sampling (ENBS). This tutorial presents a general-purpose algorithm for computing the ENBS for collecting survival data along with a step-by-step implementation in R. The algorithm is based on recently published methods for simulating survival data and computing expected value of sample information that do not rely on the survival data to follow any particular parametric distribution and that can take into account any arbitrary censoring process. We demonstrate in a case study based on a previous cancer technology appraisal that ENBS calculations are useful not only for designing new studies but also for optimizing reimbursement decisions for new health technologies based on immature evidence from ongoing trials.

Suggested Citation

  • Mathyn Vervaart, 2024. "Calculating the Expected Net Benefit of Sampling for Survival Data: A Tutorial and Case Study," Medical Decision Making, , vol. 44(7), pages 719-741, October.
    • Handle: RePEc:sae:medema:v:44:y:2024:i:7:p:719-741
    DOI: 10.1177/0272989X241279459
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    References listed on IDEAS

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    1. Mathyn Vervaart & Eline Aas & Karl P. Claxton & Mark Strong & Nicky J. Welton & Torbjørn Wisløff & Anna Heath, 2023. "General-Purpose Methods for Simulating Survival Data for Expected Value of Sample Information Calculations," Medical Decision Making, , vol. 43(5), pages 595-609, July.
    2. Mathyn Vervaart & Mark Strong & Karl P. Claxton & Nicky J. Welton & Torbjørn Wisløff & Eline Aas, 2022. "An Efficient Method for Computing Expected Value of Sample Information for Survival Data from an Ongoing Trial," Medical Decision Making, , vol. 42(5), pages 612-625, July.
    3. Anna Heath & Mark Strong & David Glynn & Natalia Kunst & Nicky J. Welton & Jeremy D. Goldhaber-Fiebert, 2022. "Simulating Study Data to Support Expected Value of Sample Information Calculations: A Tutorial," Medical Decision Making, , vol. 42(2), pages 143-155, February.
    4. A. E. Ades & G. Lu & K. Claxton, 2004. "Expected Value of Sample Information Calculations in Medical Decision Modeling," Medical Decision Making, , vol. 24(2), pages 207-227, March.
    5. Susan C. Griffin & Karl P. Claxton & Stephen J. Palmer & Mark J. Sculpher, 2011. "Dangerous omissions: the consequences of ignoring decision uncertainty," Health Economics, John Wiley & Sons, Ltd., vol. 20(2), pages 212-224, February.
    6. Karl Claxton & John Posnett, "undated". "An Economic Approach to Clinical Trial Design and Research Priority Setting," Discussion Papers 96/19, Department of Economics, University of York.
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