• Article

Physician-specific maximum acceptable risk in personalized medicine Implications for medical decision making

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.


Boeri, M., McMichael, A. J., Kane, J. P. M., O'Neill, F. A., & Kee, F. (2018). Physician-specific maximum acceptable risk in personalized medicine: Implications for medical decision making. Medical Decision Making, 38(5), 593-600. [272989X18758279]. https://doi.org/10.1177/0272989X18758279

DOI Links