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

Psychosocial Factors Are Associated With Risk Acceptance in Upper Extremity Patients

BACKGROUND

Patients who help choose their health strategies are more adherent and achieve better health. An important role of the clinician is to verify that a patient's expressed preferences are consistent with what matters most to the patient and not muddled by common misconceptions about symptoms or conditions. Patient choices are influenced by estimation of the potential benefits and potential harms of a given intervention. One method for quantifying these estimations is the concept of maximum acceptable risk (MAR), or the maximum risk that subjects are willing to accept in exchange for a given therapeutic benefit. This study addressed the hypothesis that misconceptions due to unhelpful cognitive bias regarding pain are associated with risk acceptance among people seeking care for an upper extremity condition.

METHODS

We invited 140 new adult patients visiting an upper extremity specialist to complete a survey including demographics, pain intensity, depression and anxiety symptoms, catastrophic thinking, activity limitations, and MAR. Trauma or nontrauma diagnosis was obtained from the treating clinician and recorded by the research assistant. We used bivariate and linear regression analyses to identify factors associated with MAR among this population.

RESULTS

Accounting for potential confounding in multivariable analysis, higher MAR was associated with older age and greater catastrophic thinking.

CONCLUSIONS

Specialists can be aware that people with more unhelpful cognitive biases may be willing to take more risk. Vigilance for common misconceptions and gentle, incremental reorientation of those misconceptions can increase the probability that people will choose options consistent with what matters most to them.

Accounting for Preference Heterogeneity in Discrete-Choice Experiments: An ISPOR Special Interest Group Report

OBJECTIVES

Discrete choice experiments (DCEs) are increasingly used to elicit preferences for health and healthcare. Although many applications assume preferences are homogenous, there is a growing portfolio of methods to understand both explained (because of observed factors) and unexplained (latent) heterogeneity. Nevertheless, the selection of analytical methods can be challenging and little guidance is available. This study aimed to determine the state of practice in accounting for preference heterogeneity in the analysis of health-related DCEs, including the views and experiences of health preference researchers and an overview of the tools that are commonly used to elicit preferences.

METHODS

An online survey was developed and distributed among health preference researchers and nonhealth method experts, and a systematic review of the DCE literature in health was undertaken to explore the analytical methods used and summarize trends.

RESULTS

Most respondents (n = 59 of 70, 84%) agreed that accounting for preference heterogeneity provides a richer understanding of the data. Nevertheless, there was disagreement on how to account for heterogeneity; most (n = 60, 85%) stated that more guidance was needed. Notably, the majority (n = 41, 58%) raised concern about the increasing complexity of analytical methods. Of the 342 studies included in the review, half (n = 175, 51%) used a mixed logit with continuous distributions for the parameters, and a third (n = 110, 32%) used a latent class model.

CONCLUSIONS

Although there is agreement about the importance of accounting for preference heterogeneity, there are noticeable disagreements and concerns about best practices, resulting in a clear need for further analytical guidance.

Quantifying Physician Preferences for Systemic Atopic Dermatitis Treatments Using a Discrete-Choice Experiment

Introduction

As research continues, new drugs will no doubt be added to the current pool of treatments for moderate-to-severe atopic dermatitis (AD). This raises the need for studies to determine prescriber preferences for different pharmacological options and the factors that influence their choice of treatment. Here we aim to explore physician preferences in the systemic treatment of moderate-to-severe AD, identify the sociodemographic characteristics that can influence physician preferences, and evaluate their satisfaction with current AD therapies.

Methods

A discrete-choice experiment (DCE) survey was administered to physicians treating patients with AD in Spain. Results were analyzed using a conditional logit model to estimate the relative importance of each attribute and the maximum risk accepted to achieve therapeutic benefit.

Results

A total of 28 respondents completed the DCE survey (67.9% female, mean age 45.9 years). Participants identified objective clinical efficacy and risk of severe adverse events (AEs) as the most important attributes, followed by improvement in sleep and pruritus and faster onset of action from the start of the treatment. Respondents gave less importance to mode of administration and therapeutic benefit in other atopic conditions. Respondents were willing to accept an increased risk of severe AEs and mild-to-moderate AEs leading to treatment discontinuation due to intolerance in order to obtain improvements in efficacy, sleep, and pruritus, and long-term clinical benefit.

Conclusion

Our findings can help prescribers choose the most appropriate systemic AD therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13555-022-00723-z.

Towards Personalising the Use of Biologics in Rheumatoid Arthritis: A Discrete Choice Experiment

Introduction

There have been promising developments in technologies and associated algorithm-based prescribing (‘stratified approach’) to target biologics to sub-groups of people with rheumatoid arthritis (RA). The acceptability of using an algorithm-guided approach in practice is likely to depend on various factors.

Objective

This study quantified preferences for an algorithm-guided approach to prescribing biologics (termed ‘biologic calculator’).

Methods

An online discrete choice experiment (DCE) was designed to elicit preferences from patients and the public for using a ‘biologic calculator’ compared with conventional prescribing. Treatment approaches were described by five attributes: delay to starting treatment; positive and negative predictive value (PPV/NPV); risk of infection; and cost saving to the UK national health service. Each survey contained six choice sets asking respondents to select their preferred option from two hypothetical biologic calculators or conventional prescribing. Background questions included sociodemographics, health status and healthcare experiences. DCE data were analysed using mixed logit models.

Results

Completed choice data were collected from 292 respondents (151 patients with RA and 142 members of the public). PPV, NPV and risk of infection were the most highly valued attributes to respondents deciding between prescribing strategies.

Conclusion

Respondents were generally receptive to personalised medicine in RA, but researchers developing personalised approaches should pay close attention to generating evidence on both the PPV and the NPV of their technologies.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40271-021-00533-z.