Data Sheet 1_Combining unequal variance signal detection theory with the health belief model to optimize shared decision making in tinnitus patients: part 1—model development.docx

IntroductionThe results from different Cochrane studies justify considerable professional equipoise concerning different treatment options for tinnitus. In case of professional equipoise, Shared Decision Making (SDM) is an indispensable tool for guiding patients to the intervention that best fits their needs. To improve SDM we developed a method to assess the accuracy and utility of decisions made by tinnitus patients when freely choosing between different treatment options during their patient journey. The different treatment options were audiological care and psychosocial counseling.
MethodsWe developed a statistical model by combining Signal Detection Theory (SDT) with the Health Belief Model (HBM). HBM states that perceived severity of an illness is strongly related to sick-role behavior. As proxies for perceived severity, we selected hearing loss and Tinnitus Handicap Inventory (THI) score at baseline. Next, we used these proxies as predictors in linear regression models based on SDT to determine the likelihood ratio of true positive decisions (choosing a treatment option and experiencing an improvement of more than 7 points in THI-score) and false positive decisions (choosing a treatment option and experiencing an improvement of less than 7 points in THI-score) for audiological care and psychosocial counseling, respectively. Data was gathered in a prospective cohort of 145 adults referred for tinnitus care to an outpatient audiology clinic in the Netherlands. The participants were asked to decide freely on uptake of audiological care (provision of hearing aids with or without a sound generator) and uptake of psychosocial counseling. Logistic regression with Bayesian inference was used to determine the cumulative distribution functions and the probability density functions of true positive decisions and false positive decisions as function of hearing loss and baseline THI-score for both treatment options, respectively. With the cumulative distribution functions, we determined the accuracy of the decisions. With the probability density functions we calculated the likelihood ratios of true positive decisions versus false positive decisions as function of hearing loss and baseline THI-score. These likelihood ratio functions allow assessment of the utility of the decisions by relating a decision criterion to perceived benefits and costs.
ResultsBaseline THI-score drives decisions about psychosocial counseling and hearing loss drives decisions about audiological care. Decisions about psychosocial counseling are more accurate than decisions about audiological care. Both decisions have a low accuracy (0.255 for audiological care and  − 0.429 for psychosocial counseling), however. For decisions about audiological care the unbiased decision criterion is 37 dB(HL), meaning that a lenient decision criterion (likelihood ratio < 1) is adopted by patients with a hearing loss below 37 dB and a strict criterion (likelihood ratio > 1) by patients with a hearing loss exceeding 37 dB. For psychosocial counseling uptake the decision criterion is always strict, regardless of baseline THI-score. The distributions of the populations, that do and do not experience a clinically important change in THI-score, have unequal variances for psychosocial counseling, while they have almost equal variances for audiological care.
DiscussionCombining SDT and HBM can help assess accuracy and utility of patient decisions and thus may provide valuable information that can help to improve SDM by combining patient related outcome measures, decision drivers, and perceived benefits and costs of a treatment.

引言
多项考克兰（Cochrane）研究的结果表明，针对耳鸣的不同治疗方案仍存在相当程度的专业均衡（professional equipoise）。当处于专业均衡状态时，共同决策（Shared Decision Making，SDM）是帮助患者选择最贴合自身需求的干预措施的不可或缺的工具。为优化共同决策，我们开发了一种方法，用于评估耳鸣患者在诊疗过程中自主选择不同治疗方案时所做决策的准确性与效用。本研究涉及的两类治疗方案分别为听力学照护与社会心理咨询。

方法
我们将信号检测论（Signal Detection Theory，SDT）与健康信念模型（Health Belief Model，HBM）相结合，构建了统计模型。健康信念模型指出，个体对疾病的感知严重性与患者角色行为密切相关。我们选取基线听力损失与基线耳鸣残障量表（Tinnitus Handicap Inventory，THI）得分作为感知严重性的替代指标。随后，我们以这些替代指标作为预测变量，构建基于信号检测论的线性回归模型，分别针对听力学照护与社会心理咨询，计算真阳性决策（选择某一治疗方案且耳鸣残障量表得分改善超过7分）与假阳性决策（选择某一治疗方案但耳鸣残障量表得分改善不足7分）的似然比。
本研究数据来源于荷兰一家门诊听力学诊所招募的145名成年耳鸣就诊患者的前瞻性队列。受试者可自主选择是否接受听力学照护（配备带或不带声发生器的助听器）以及社会心理咨询服务。我们采用基于贝叶斯推断的逻辑回归模型，分别针对两种治疗方案，计算以基线听力损失与基线THI得分为自变量的真阳性决策与假阳性决策的累积分布函数与概率密度函数。基于累积分布函数，我们可评估决策的准确性；基于概率密度函数，我们可计算以基线听力损失与基线THI得分为自变量的真阳性决策与假阳性决策的似然比。上述似然比函数可通过将决策阈值与感知到的治疗收益及成本相挂钩，实现对决策效用的评估。

结果
基线THI得分是影响社会心理咨询决策的核心因素，而听力损失则是影响听力学照护决策的核心因素。社会心理咨询的决策准确性高于听力学照护的决策准确性，但两类决策的准确性均较低：听力学照护的决策准确性为0.255，社会心理咨询的决策准确性为−0.429。针对听力学照护的决策，无偏决策阈值为37分贝（听力损失，HL），即基线听力损失低于37分贝的患者会采用宽松决策阈值（似然比<1），而听力损失超过37分贝的患者则采用严格决策阈值（似然比>1）。而对于社会心理咨询的选择，无论基线THI得分如何，患者始终采用严格决策阈值。在社会心理咨询场景下，出现与未出现临床意义上THI得分显著改善的受试者群体的分布方差并不相等；而在听力学照护场景下，两类群体的分布方差则近似相等。

讨论
将信号检测论与健康信念模型相结合，可有效评估患者决策的准确性与效用，通过整合患者相关结局指标、决策驱动因素以及治疗的感知收益与成本，为优化共同决策提供有价值的参考依据。