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从离散选择实验获益-风险研究中计算同时最大可接受风险阈值(SMART)的方法。

Method for Calculating the Simultaneous Maximum Acceptable Risk Threshold (SMART) from Discrete-Choice Experiment Benefit-Risk Studies.

机构信息

University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Duke Clinical Research Institute, Durham, NC, USA.

出版信息

Med Decis Making. 2023 Feb;43(2):227-238. doi: 10.1177/0272989X221132266. Epub 2022 Nov 3.

Abstract

BACKGROUND

Medical decisions require weighing expected benefits of treatment against multiple adverse outcomes under uncertainty (i.e., risks) that must be accepted as a bundle. However, conventional maximum acceptable risk (MAR) estimates derived from discrete-choice experiment benefit-risk studies evaluate the acceptance of individual risks, assuming other risks are fixed, potentially leading decision makers to misinterpret levels of risk acceptance.

DESIGN

Using simulations and a published discrete-choice experiment, we demonstrate a method for identifying multidimensional risk-tolerance measures given a treatment level of benefit.

RESULTS

Simultaneous Maximum Acceptable Risk Thresholds (SMART) represents combinations of risks that would be jointly accepted in exchange for specific treatment benefits. The framework shows how the expectation of utility associated with treatments that involve multiple risks are related even when preferences for potential adverse events are independent. We find that the form of the marginal effects of adverse-event probabilities on the expected utility of treatment determines the magnitude of differences between SMART and conventional single-outcome MAR estimates.

LIMITATIONS

Preferences for potential adverse events not considered in a study or preferences for adverse-event attributes held constant in risk-tolerance calculations may affect estimated risk tolerance. Further research is needed to understand the right balance between realistically reflecting clinical treatments with many potential adverse events and the cognitive burden of evaluating risk-risk tradeoffs in research and in practice.

CONCLUSIONS AND IMPLICATIONS

SMART analysis should be considered in preference studies evaluating the joint acceptance of multiple potential adverse events.

HIGHLIGHTS

Conventional approaches to calculate maximum-acceptable risk (MAR) using discrete-choice experiment data account for 1 adverse-event risk at a time, requiring that decision makers infer the acceptability of treatments when patients are exposed to multiple risks simultaneously.The Simultaneous Maximum Acceptable Risk Threshold (SMART) maps combinations of adverse-event risks that would be jointly acceptable given a specific treatment benefit and provides a transparent and precise portrayal of acceptance of multiple risks.Risk levels that would be accepted using individual MAR estimates might not be acceptable when simultaneous risks are considered, especially when marginal expected disutility of risk is decreasing nonlinearly with risk probabilities.Preference researchers should calculate SMARTs in any discrete-choice study in which 2 or more adverse-event risks are presented, particularly if risk preferences are nonlinear.

摘要

背景

医疗决策需要权衡治疗的预期收益与不确定性下的多种不良后果(即风险),这些风险必须作为一个整体来接受。然而,传统的最大可接受风险(MAR)估计值是从离散选择实验效益风险研究中得出的,这些研究评估了对个体风险的接受程度,假设其他风险是固定的,这可能导致决策者错误地解释风险接受程度。

设计

我们使用模拟和已发表的离散选择实验,演示了一种在给定治疗水平效益的情况下确定多维风险容忍度度量的方法。

结果

同时可接受的最大风险阈值(SMART)代表了为换取特定治疗益处而共同接受的风险组合。该框架展示了即使对潜在不良事件的偏好是独立的,涉及多种风险的治疗的预期效用与相关的同时最大可接受风险阈值之间的关系。我们发现,不良事件概率对治疗预期效用的边际效应的形式决定了 SMART 与传统单一结果 MAR 估计值之间差异的大小。

局限性

研究中未考虑的潜在不良事件偏好或风险容忍度计算中保持不变的不良事件属性偏好可能会影响估计的风险容忍度。需要进一步研究以了解在研究和实践中,真实反映具有多种潜在不良事件的临床治疗与评估风险风险权衡的认知负担之间的正确平衡。

结论和意义

在评估多个潜在不良事件的联合接受度的偏好研究中,应考虑 SMART 分析。

重点

使用离散选择实验数据计算最大可接受风险(MAR)的传统方法一次只考虑 1 种不良事件风险,要求决策者在患者同时面临多种风险时推断治疗的可接受性。同时可接受的最大风险阈值(SMART)映射了在特定治疗益处下可同时接受的不良事件风险组合,并提供了对多种风险接受度的透明和精确描述。当同时考虑风险时,使用个体 MAR 估计值接受的风险水平可能不可接受,尤其是当风险概率的边际预期负效用呈非线性下降时。如果呈现了 2 个或更多不良事件风险,偏好研究人员应在任何离散选择研究中计算 SMART,特别是如果风险偏好是非线性的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8429/9827493/c21f2a996ca7/10.1177_0272989X221132266-fig1.jpg

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