Department of Medicine and Department of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada (GT, GN, MDK)
Toronto General Research Institute, University Health Network, Toronto, Ontario, Canada (GT, KEB, MDK)
Med Decis Making. 2012 Jan-Feb;32(1):11-30. doi: 10.1177/0272989X11407203. Epub 2011 Jun 8.
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.
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.
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.
The scoring system together with the PORPUS comprise an indirect utility instrument, the PORPUS-U, which can be used in clinical and research settings.
此前，我们开发了一种前列腺癌（PC）特异性健康状况分类系统，即患者导向前列腺效用量表（PORPUS）。在这项研究中，我们开发了一种评分系统，以便能够从 PORPUS 间接计算效用。
我们对 234 名前列腺癌门诊患者进行了访谈，包括新诊断和转移性疾病患者，以获得 PORPUS 的 10 个属性的每个属性的 4 到 6 个水平以及 10 个角状态（一个属性的最差水平，9 个属性的最佳水平）的评分量表（RS）值。患者还完成了 4 个多属性状态（勃起功能障碍和疼痛角状态、轻度和重度前列腺症状）的标准赌博（SG）和 RS 任务。我们使用多属性状态的 RS 和 SG 评分来确定映射值到效用的风险厌恶函数。然后，我们使用 10 个 PORPUS 属性的每个水平的单个属性不效用以及角状态的不效用，测试了 15 种不同的策略来估计多属性效用函数（MAUF）。SG 在 4 个多属性状态上的预测的均方根误差（RMSE）用于识别最佳策略和评分系统。
最佳策略的 RMSE 为 0.06。将从之前发表的 2 项研究（n=248 和 n=141）中的 248 名和 141 名患者中获得的 2 个多属性状态的 MAUF 预测效用的平均值与直接得出的 SG 效用进行比较，支持了 MAUF 的有效性。
评分系统与 PORPUS 一起构成了一种间接效用工具，即 PORPUS-U，可用于临床和研究环境。
