Abstract
Benchmark surveillance tests for detecting disease progression (eg, biopsies, endoscopies) in early-stage chronic noncommunicable diseases (eg, cancer, lung diseases) are usually burdensome. For detecting progression timely, patients undergo invasive tests planned in a fixed one-size-fits-all manner (eg, annually). We aim to present personalized test schedules based on the risk of disease progression, that optimize the burden (the number of tests) and the benefit (shorter time delay in detecting progression is better) better than fixed schedules, and enable shared decision making. Our motivation comes from the problem of scheduling biopsies in prostate cancer surveillance. Using joint models for time-to-event and longitudinal data, we consolidate patients' longitudinal data (eg, biomarkers) and results of previous tests, into individualized future cumulative-risk of progression. We then create personalized schedules by planning tests on future visits where the predicted cumulative-risk is above a threshold (eg, 5% risk). We update personalized schedules with data gathered over follow-up. To find the optimal risk threshold, we minimize a utility function of the expected number of tests (burden) and expected time delay in detecting progression (shorter is beneficial) for different thresholds. We estimate these two in a patient-specific manner for following any schedule, by utilizing a patient's predicted risk profile. Patients/doctors can employ these quantities to compare personalized and fixed schedules objectively and make a shared decision of a test schedule.
| Original language | English |
|---|---|
| Pages (from-to) | 2115-2131 |
| Number of pages | 17 |
| Journal | Statistics in Medicine |
| Volume | 41 |
| Issue number | 12 |
| Early online date | 10 Feb 2022 |
| DOIs | |
| Publication status | Published - 30 May 2022 |
Bibliographical note
Funding Information:information Erasmus Medisch Centrum, Nederlandse Organisatie voor Wetenschappelijk Onderzoek, 016.146.301Anirudh Tomer and Dimitris Rizopoulos would like to acknowledge support by Nederlandse Organisatie voor Wetenschappelijk Onderzoek (the national research council of the Netherlands) VIDI grant nr. 016.146.301, and Erasmus University Medical Center funding. Part of this work was carried out on the Dutch national e-infrastructure with the support of SURF Cooperative. The authors also thank the Erasmus University Medical Center's Cancer Computational Biology Center for giving access to their IT-infrastructure and software that was used for the computations and data analysis in this study. Last, The authors also like to thank the PRIAS consortium for enabling this research project.
Publisher Copyright:
© 2022 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
