将扩展前列腺癌指数综合指数映射到 EQ5D 效用，为前列腺癌的经济评估提供信息：NRG/RTOG 0415 的二次分析。

Mapping expanded prostate cancer index composite to EQ5D utilities to inform economic evaluations in prostate cancer: Secondary analysis of NRG/RTOG 0415.

机构信息

Department of Pharmaceutical Health Services Research, University of Maryland School of Pharmacy, Baltimore, MD, United States of America.

NRG Oncology Statistics and Data Management Center, Philadelphia, PA, United States of America.

出版信息

PLoS One. 2021 Apr 14;16(4):e0249123. doi: 10.1371/journal.pone.0249123. eCollection 2021.

DOI:10.1371/journal.pone.0249123

PMID:33852571

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8046237/

Abstract

PURPOSE

The Expanded Prostate Cancer Index Composite (EPIC) is the most commonly used patient reported outcome (PRO) tool in prostate cancer (PC) clinical trials, but health utilities associated with the different health states assessed with this tool are unknown, limiting our ability to perform cost-utility analyses. This study aimed to map EPIC tool to EuroQoL-5D-3L (EQ5D) to generate EQ5D health utilities.

METHODS AND MATERIALS

This is a secondary analysis of a prospective, randomized non-inferiority clinical trial, conducted between 04/2006 and 12/2009 at cancer centers across the United States, Canada, and Switzerland. Eligible patients included men >18 years with a known diagnosis of low-risk PC. Patient HRQoL data were collected using EPIC and health utilities were obtained using EQ5D. Data were divided into an estimation sample (n = 765, 70%) and a validation sample (n = 327, 30%). The mapping algorithms that capture the relationship between the instruments were estimated using ordinary least squares (OLS), Tobit, and two-part models. Five-fold cross-validation (in-sample) was used to compare the predictive performance of the estimated models. Final models were selected based on root mean square error (RMSE).

RESULTS

A total of 565 patients in the estimation sample had complete information on both EPIC and EQ5D questionnaires at baseline. Mean observed EQ5D utility was 0.90±0.13 (range: 0.28-1) with 55% of patients in full health. OLS models outperformed their counterpart Tobit and two-part models for all pre-determined model specifications. The best model fit was: "EQ5D utility = 0.248541 + 0.000748*(Urinary Function) + 0.001134*(Urinary Bother) + 0.000968*(Hormonal Function) + 0.004404*(Hormonal Bother)- 0.376487*(Zubrod) + 0.003562*(Urinary Function*Zubrod)"; RMSE was 0.10462.

CONCLUSIONS

This is the first study to identify a comprehensive set of mapping algorithms to generate EQ5D utilities from EPIC domain/ sub-domain scores. The study results will help estimate quality-adjusted life-years in PC economic evaluations.

摘要

目的

前列腺癌指数综合量表（EPIC）是前列腺癌临床试验中最常用的患者报告结局（PRO）工具，但该工具评估的不同健康状态的健康效用尚不清楚，这限制了我们进行成本效用分析的能力。本研究旨在将 EPIC 工具与 EuroQoL-5D-3L（EQ5D）相匹配，以生成 EQ5D 健康效用。

方法和材料

这是一项在美国、加拿大和瑞士的癌症中心进行的前瞻性、随机非劣效性临床试验的二次分析，该试验于 2006 年 4 月至 2009 年 12 月进行。合格的患者包括年龄>18 岁且已知患有低危前列腺癌的男性。使用 EPIC 收集患者的 HRQoL 数据，并使用 EQ5D 获得健康效用。数据分为估计样本（n=765，70%）和验证样本（n=327，30%）。使用普通最小二乘法（OLS）、Tobit 和两部分模型来估计捕获仪器之间关系的映射算法。使用五倍交叉验证（内部样本）比较估计模型的预测性能。最终模型基于均方根误差（RMSE）选择。

结果

在估计样本中，共有 565 名患者在基线时有完整的 EPIC 和 EQ5D 问卷信息。观察到的平均 EQ5D 效用为 0.90±0.13（范围：0.28-1），55%的患者处于完全健康状态。对于所有预先确定的模型规格，OLS 模型均优于其 Tobit 和两部分模型。最佳模型拟合为：“EQ5D 效用=0.248541+0.000748*（尿功能）+0.001134*（尿困扰）+0.000968*（激素功能）+0.004404*（激素困扰）-0.376487*（Zubrod）+0.003562*（尿功能*Zubrod）”；RMSE 为 0.10462。