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Development and validation of a utility weighting function for the patient-oriented prostate utility scale (PORPUS)

机译:面向患者的前列腺公用事业量表(PORPUS)的公用事业加权功能的开发和验证

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Background. Previously, we developed a prostate cancer (PC)-specific health state classification system, the Patient Oriented Prostate Utility Scale (PORPUS). In this study, we developed a scoring system to allow indirect calculation of utilities from the PORPUS. Methods. We interviewed 234 PC outpatients, including those with newly diagnosed and metastatic disease, to obtain rating scale (RS) values on 4 to 6 levels of each of the 10 attributes of the PORPUS, and on 10 corner states (worst level on 1 attribute, best on 9). Patients also completed standard gamble (SG) and RS tasks on 4 multiattribute states (impotence and pain corner states, mild and severe PC symptoms). We used the RS and SG scores for multiattribute states to determine a risk aversion function for mapping values to utilities. We then tested 15 different strategies to estimate the multiattribute utility function (MAUF), using the single attribute disutilities for each level of the 10 PORPUS attributes, and the disutilities for the corner states. The root mean squared error (RMSE) of prediction of the SG on the 4 multiattribute states was used to identify the optimal strategy and scoring system. Results. The optimal strategy gave an RMSE of 0.06. Comparison of mean MAUF-predicted utilities to directly elicited SG utilities for the 2 multiattribute states from patients in 2 previously published studies (n = 248 and n = 141) supported the validity of the MAUF. Conclusions. The scoring system together with the PORPUS comprise an indirect utility instrument, the PORPUS-U, which can be used in clinical and research settings.
机译:背景。以前,我们开发了前列腺癌(PC)特定的健康状态分类系统,即以患者为导向的前列腺实用量表(PORPUS)。在这项研究中,我们开发了一个评分系统,可以从PORPUS间接计算效用。方法。我们采访了234位PC门诊患者,包括患有新诊断和转移性疾病的门诊患者,以获取PORPUS 10个属性中每个属性的4到6个等级以及10个边缘状态(1个属性的最差等级,最好在9)。患者还完成了4种多属性状态(阳pot和疼痛角状态,轻度和严重PC症状)的标准赌博(SG)和RS任务。我们将RS和SG得分用于多属性状态,以确定将值映射到公用事业的风险规避函数。然后,我们针对15个PORPUS属性的每个级别使用单一属性无效性以及对边角状态的无效性,测试了15种不同的策略来估计多属性效用函数(MAUF)。 SG的4个多属性状态的预测的均方根误差(RMSE)用于确定最佳策略和评分系统。结果。最佳策略的RMSE为0.06。在2项先前发表的研究(n = 248和n = 141)中,比较了两种患者的2种多属性状态的平均MAUF预测效用与直接引起SG效用的比较,支持了MAUF的有效性。结论计分系统与PORPUS一起构成间接实用工具PORPUS-U,可在临床和研究环境中使用。

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