Trust Me, I'm a Doctor?

📅 2026-05-01
📈 Citations: 0
Influential: 0
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career value

176K/year
🤖 AI Summary
This study addresses the gap between evidence-based medicine and personalized clinical practice by examining whether physicians’ experience-driven treatment decisions outperform the average optimal therapy recommended by randomized controlled trials (RCTs), which often overlook individual heterogeneity. By integrating RCT and observational cohort data from the same population, the authors propose a “gain score” to quantify the benefit of physician-assigned strategies relative to trial-based recommendations. Within a nested study design, they derive sharp nonparametric bounds on the proportion of patients for whom physician strategies are superior—a result established for the first time without parametric assumptions. Leveraging causal inference and partial identification techniques, this work provides a data-driven framework to determine when clinical discretion should supersede population-average guidelines, thereby bridging the divide between standardized evidence and individualized care.
📝 Abstract
Clinical trials usually target average treatment effects, but treatment decisions are made for individuals. This tension motivates a common criticism of evidence-based medicine: a treatment that is beneficial on average may be inappropriate for a particular patient, and skilled physicians may outperform rigid adherence to the strategy that performed best in a randomized trial. We consider how randomized and observational data from the same target population can be used to assess that possibility. Specifically, we study settings in which a randomized trial is nested within an observational cohort, so that outcomes are observed under treatment, control, and usual care. We ask what the observed data can reveal about how often physicians outperform the strategy suggested by the trial. We define a gain score to formalize this comparison and derive sharp bounds on the proportion of physicians whose personal strategies perform at least as well as, or better than, always choosing the better performing treatment from the trial. These results shed light on when clinical data support relying on physician discretion over the trial-average recommendation and when stronger justification is required.
Problem

Research questions and friction points this paper is trying to address.

individualized treatment
average treatment effect
physician discretion
evidence-based medicine
treatment heterogeneity
Innovation

Methods, ideas, or system contributions that make the work stand out.

gain score
nested trial design
individualized treatment
sharp bounds
evidence-based medicine
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Zach Shahn
Department of Epidemiology and Biostatistics, CUNY Graduate School of Public Health and Health Policy
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Mats Stensrud
Institute of Mathematics, École Polytechnique Fédérale de Lausanne (EPFL)