Physician-Specific Maximum Acceptable Risk in Personalized Medicine: Implications for Medical Decision Making

Marco Boeri, Alan J McMichael, Joseph P M Kane, Francis A O'Neill, Frank Kee

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BACKGROUND: In discrete-choice experiments (DCEs), respondents are presented with a series of scenarios and asked to select their preferred choice. In clinical decision making, DCEs allow one to calculate the maximum acceptable risk (MAR) that a respondent is willing to accept for a one-unit increase in treatment efficacy. Most published studies report the average MAR for the whole sample, without conveying any information about heterogeneity. For a sample of psychiatrists prescribing drugs for a series of hypothetical patients with schizophrenia, this article demonstrates how heterogeneity accounted for in the DCE modeling can be incorporated in the derivation of the MAR.

METHODS: Psychiatrists were given information about a group of patients' responses to treatment on the Positive and Negative Syndrome Scale (PANSS) and the weight gain associated with the treatment observed in a series of 26 vignettes. We estimated a random parameters logit (RPL) model with treatment choice as the dependent variable.

RESULTS: Results from the RPL were used to compute the MAR for the overall sample. This was found to be equal to 4%, implying that, overall, psychiatrists were willing to accept a 4% increase in the risk of an adverse event to obtain a one-unit improvement of symptoms - measured on the PANSS. Heterogeneity was then incorporated in the MAR calculation, finding that MARs ranged between 0.5 and 9.5 across the sample of psychiatrists.

LIMITATIONS: We provided psychiatrists with hypothetical scenarios, and their MAR may change when making decisions for actual patients.

CONCLUSIONS: This analysis aimed to show how it is possible to calculate physician-specific MARs and to discuss how MAR heterogeneity could have implications for medical practice.

Original languageEnglish
Pages (from-to)593-600
Number of pages8
JournalMedical Decision Making
Issue number5
Early online date03 Apr 2018
Publication statusPublished - Jul 2018

Bibliographical note

Boeri, Marco McMichael, Alan J Kane, Joseph P M O'Neill, Francis A Kee, Frank eng MR/K023241/1/Medical Research Council/United Kingdom Research Support, Non-U.S. Gov't Med Decis Making. 2018 Jul;38(5):593-600. doi: 10.1177/0272989X18758279. Epub 2018 Apr 3.


  • Antipsychotic Agents/adverse effects/*therapeutic use *Choice Behavior *Clinical Decision-Making *Health Knowledge, Attitudes, Practice Humans Interviews as Topic Logistic Models Physicians/*psychology Precision Medicine Risk Schizophrenia/*drug therapy Schizophrenic Psychology Severity of Illness Index Surveys and Questionnaires *discrete choice experiments *maximum-acceptable risk *personalized medicine *preference analysis *psychiatry


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