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Irreversible treatment decisions under consideration of the research and development pipeline for new therapies

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  • Steven Shechter
  • Oguzhan Alagoz
  • Mark Roberts

Abstract

This article addresses a topic not considered in previous models of patient treatment: the possible downstream availability of improved treatment options coming out of the medical research and development (R&D) pipeline. We provide clinical examples in which a patient may prefer to wait and take the chance that an improved therapy comes to market rather than choose an irreversible treatment option that has serious quality of life ramifications and would render future treatment discoveries meaningless for that patient. We then develop a Markov decision process model of the optimal time to initiate treatment, which incorporates uncertainty around the development of new therapies and their effects. After deriving structural properties for the model, we provide a numerical example that demonstrates how models that do not have any foresight of the R&D pipeline may result in optimal policies that differ from models that have such foresight, implying erroneous decisions in the former models. Our example quantifies the effects of such errors.

Suggested Citation

  • Steven Shechter & Oguzhan Alagoz & Mark Roberts, 2010. "Irreversible treatment decisions under consideration of the research and development pipeline for new therapies," IISE Transactions, Taylor & Francis Journals, vol. 42(9), pages 632-642.
    • Handle: RePEc:taf:uiiexx:v:42:y:2010:i:9:p:632-642
    DOI: 10.1080/07408170903468589
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    Cited by:

    1. Mabel C. Chou & Mahmut Parlar & Yun Zhou, 2017. "Optimal Timing to Initiate Medical Treatment for a Disease Evolving as a Semi-Markov Process," Journal of Optimization Theory and Applications, Springer, vol. 175(1), pages 194-217, October.
    2. Shan Liu & Margaret L. Brandeau & Jeremy D. Goldhaber-Fiebert, 2017. "Optimizing patient treatment decisions in an era of rapid technological advances: the case of hepatitis C treatment," Health Care Management Science, Springer, vol. 20(1), pages 16-32, March.
    3. Jonathan E. Helm & Mariel S. Lavieri & Mark P. Van Oyen & Joshua D. Stein & David C. Musch, 2015. "Dynamic Forecasting and Control Algorithms of Glaucoma Progression for Clinician Decision Support," Operations Research, INFORMS, vol. 63(5), pages 979-999, October.

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