Statistics and Its Interface

Volume 16 (2023)

Number 3

Confidence in the treatment decision for an individual patient: strategies for sequential assessment

Pages: 475 – 491

DOI: https://dx.doi.org/10.4310/22-SII737

Authors

Nina Orwitz (Grossman School of Medicine, New York University, New York, N.Y., U.S.A.)

Thaddeus Tarpey (Grossman School of Medicine, New York University, New York, N.Y., U.S.A.)

Eva Petkova (Grossman School of Medicine, New York University, New York, N.Y., U.S.A.)

Abstract

Evolving medical technologies have motivated the development of treatment decision rules (TDRs) that incorporate complex, costly data (e.g., imaging). In clinical practice, we aim for TDRs to be valuable by reducing unnecessary testing while still identifying the best possible treatment for a patient. Regardless of how well any TDR performs in the target population, there is an associated degree of uncertainty about its optimality for a specific patient. In this paper, we aim to quantify, via a confidence measure, the uncertainty in a TDR as patient data from sequential procedures accumulate in real-time. We first propose estimating confidence using the distance of a patient’s vector of covariates to a treatment decision boundary, with further distances corresponding to higher certainty. We further propose measuring confidence through the conditional probabilities of ultimately (with all possible information available) being assigned a particular treatment, given that the same treatment is assigned with the patient’s currently available data or given the treatment recommendation made using only the currently available patient data. As patient data accumulate, the treatment decision is updated and confidence reassessed until a sufficiently high confidence level is achieved. We present results from simulation studies and illustrate the methods using a motivating example from a depression clinical trial. Recommendations for practical use of the measures are proposed.

Keywords

precision medicine, treatment decision rules

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This work was supported by the National Institutes of Health (NIH) training grant 5TL1TR001447-05, and by the National Institute of Mental Health (of the NIH) grant R01MH099003.

Received 23 September 2021

Accepted 24 April 2022

Published 14 April 2023