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Personalized schedules for surveillance of low‐risk prostate cancer patients

Author

Listed:
  • Anirudh Tomer
  • Daan Nieboer
  • Monique J. Roobol
  • Ewout W. Steyerberg
  • Dimitris Rizopoulos

Abstract

Low‐risk prostate cancer patients enrolled in active surveillance (AS) programs commonly undergo biopsies on a frequent basis for examination of cancer progression. AS programs employ a fixed schedule of biopsies for all patients. Such fixed and frequent schedules may schedule unnecessary biopsies. Since biopsies are burdensome, patients do not always comply with the schedule, which increases the risk of delayed detection of cancer progression. Motivated by the world's largest AS program, Prostate Cancer Research International Active Surveillance (PRIAS), we present personalized schedules for biopsies to counter these problems. Using joint models for time‐to‐event and longitudinal data, our methods combine information from historical prostate‐specific antigen levels and repeat biopsy results of a patient, to schedule the next biopsy. We also present methods to compare personalized schedules with existing biopsy schedules.

Suggested Citation

  • Anirudh Tomer & Daan Nieboer & Monique J. Roobol & Ewout W. Steyerberg & Dimitris Rizopoulos, 2019. "Personalized schedules for surveillance of low‐risk prostate cancer patients," Biometrics, The International Biometric Society, vol. 75(1), pages 153-162, March.
  • Handle: RePEc:bla:biomet:v:75:y:2019:i:1:p:153-162
    DOI: 10.1111/biom.12940
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    References listed on IDEAS

    as
    1. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. Jingyu Zhang & Brian T. Denton & Hari Balasubramanian & Nilay D. Shah & Brant A. Inman, 2012. "Optimization of Prostate Biopsy Referral Decisions," Manufacturing & Service Operations Management, INFORMS, vol. 14(4), pages 529-547, October.
    3. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    4. Turgay Ayer & Oguzhan Alagoz & Natasha K. Stout, 2012. "OR Forum---A POMDP Approach to Personalize Mammography Screening Decisions," Operations Research, INFORMS, vol. 60(5), pages 1019-1034, October.
    5. repec:dau:papers:123456789/1908 is not listed on IDEAS
    6. Dimitris Rizopoulos & Laura A. Hatfield & Bradley P. Carlin & Johanna J. M. Takkenberg, 2014. "Combining Dynamic Predictions From Joint Models for Longitudinal and Time-to-Event Data Using Bayesian Model Averaging," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1385-1397, December.
    7. Fatih Safa Erenay & Oguzhan Alagoz & Adnan Said, 2014. "Optimizing Colonoscopy Screening for Colorectal Cancer Prevention and Surveillance," Manufacturing & Service Operations Management, INFORMS, vol. 16(3), pages 381-400, July.
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