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Modelling the Survival Outcomes of Immuno-Oncology Drugs in Economic Evaluations: A Systematic Approach to Data Analysis and Extrapolation

Author

Listed:
  • Eddie Gibson

    (Wickenstones Ltd)

  • Ian Koblbauer

    (Wickenstones Ltd)

  • Najida Begum

    (Wickenstones Ltd)

  • George Dranitsaris

    (Augmentium Pharma Consulting Inc.)

  • Danny Liew

    (Monash University)

  • Phil McEwan

    (Health Economics and Outcomes Research Ltd)

  • Amir Abbas Tahami Monfared

    (Bristol-Myers Squibb
    McGill University)

  • Yong Yuan

    (Bristol-Myers Squibb)

  • Ariadna Juarez-Garcia

    (Bristol-Myers Squibb)

  • David Tyas

    (Bristol-Myers Squibb)

  • Michael Lees

    (Bristol-Myers Squibb)

Abstract

Background New immuno-oncology (I-O) therapies that harness the immune system to fight cancer call for a re-examination of the traditional parametric techniques used to model survival from clinical trial data. More flexible approaches are needed to capture the characteristic I-O pattern of delayed treatment effects and, for a subset of patients, the plateau of long-term survival. Objectives Using a systematic approach to data management and analysis, the study assessed the applicability of traditional and flexible approaches and, as a test case of flexible methods, investigated the suitability of restricted cubic splines (RCS) to model progression-free survival (PFS) in I-O therapy. Methods The goodness of fit of each survival function was tested on data from the CheckMate 067 trial of monotherapy versus combination therapy (nivolumab/ipilimumab) in metastatic melanoma using visual inspection and statistical tests. Extrapolations were validated using long-term data for ipilimumab. Results Modelled PFS estimates using traditional methods did not provide a good fit to the Kaplan–Meier (K–M) curve. RCS estimates fit the K–M curves well, particularly for the plateau phase. RCS with six knots provided the best overall fit, but RCS with one knot performed best at the plateau phase and was preferred on the grounds of parsimony. Conclusions RCS models represent a valuable addition to the range of flexible approaches available to model survival when assessing the effectiveness and cost-effectiveness of I-O therapy. A systematic approach to data analysis is recommended to compare the suitability of different approaches for different diseases and treatment regimens.

Suggested Citation

  • Eddie Gibson & Ian Koblbauer & Najida Begum & George Dranitsaris & Danny Liew & Phil McEwan & Amir Abbas Tahami Monfared & Yong Yuan & Ariadna Juarez-Garcia & David Tyas & Michael Lees, 2017. "Modelling the Survival Outcomes of Immuno-Oncology Drugs in Economic Evaluations: A Systematic Approach to Data Analysis and Extrapolation," PharmacoEconomics, Springer, vol. 35(12), pages 1257-1270, December.
    • Handle: RePEc:spr:pharme:v:35:y:2017:i:12:d:10.1007_s40273-017-0558-5
    DOI: 10.1007/s40273-017-0558-5
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    References listed on IDEAS

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    1. Patrick Royston & Paul C. Lambert, 2011. "Flexible Parametric Survival Analysis Using Stata: Beyond the Cox Model," Stata Press books, StataCorp LP, number fpsaus, March.
    2. Patrick Royston, 2001. "Flexible alternatives to the Cox model, and more," Stata Journal, StataCorp LP, vol. 1(1), pages 1-28, November.
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    Cited by:

    1. Deborah Plana & Geoffrey Fell & Brian M. Alexander & Adam C. Palmer & Peter K. Sorger, 2022. "Cancer patient survival can be parametrized to improve trial precision and reveal time-dependent therapeutic effects," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    2. Bengt Jönsson & Grace Hampson & Jonathan Michaels & Adrian Towse & J.-Matthias Graf Schulenburg & Olivier Wong, 2019. "Advanced therapy medicinal products and health technology assessment principles and practices for value-based and sustainable healthcare," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 427-438, April.

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