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Predicting Colorectal Cancer Mortality: Models to Facilitate Patient‐Physician Conversations and Inform Operational Decision Making

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  • Margret Bjarnadottir
  • David Anderson
  • Leila Zia
  • Kim Rhoads

Abstract

Having accurate, unbiased prognosis information can help patients and providers make better decisions about what course of treatment to take. Using a comprehensive dataset of all colorectal cancer patients in California, we generate predictive models that estimate short‐term and medium‐term survival probabilities for patients based on their clinical and demographic information. Our study addresses some of the contradictions in the literature about survival rates and significantly improves predictive power over the performance of any model in previously published studies.

Suggested Citation

  • Margret Bjarnadottir & David Anderson & Leila Zia & Kim Rhoads, 2018. "Predicting Colorectal Cancer Mortality: Models to Facilitate Patient‐Physician Conversations and Inform Operational Decision Making," Production and Operations Management, Production and Operations Management Society, vol. 27(12), pages 2162-2183, December.
    • Handle: RePEc:bla:popmgt:v:27:y:2018:i:12:p:2162-2183
    DOI: 10.1111/poms.12896
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    Cited by:

    1. Sun, Huan & Wang, Haiyan & Steffensen, Sonja, 2022. "Mechanism design of multi-strategy health insurance plans under asymmetric information," Omega, Elsevier, vol. 107(C).
    2. Hossein Kamalzadeh & Vishal Ahuja & Michael Hahsler & Michael E. Bowen, 2021. "An Analytics‐Driven Approach for Optimal Individualized Diabetes Screening," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 3161-3191, September.
    3. Anthony Bonifonte & Turgay Ayer & Benjamin Haaland, 2022. "An Analytics Approach to Guide Randomized Controlled Trials in Hypertension Management," Management Science, INFORMS, vol. 68(9), pages 6634-6647, September.

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