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Benchmark surveillance tests for detecting disease progression (e.g., biopsies, endoscopies) in early-stage chronic noncommunicable diseases (e.g., cancer, lung diseases) are usually burdensome. To detect progression timely, patients undergo invasive tests planned in a fixed one-size-fits-all manner (e.g., 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 (e.g., biomarkers) and results of previous tests into the 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 (e.g., 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 objectively compare personalized and fixed schedules and make a shared decision about a test schedule.
Key words: Precision medicine, dynamic predictions, joint models.
