The PROPr can be measured using different PROMIS domain item sets

IF 2.4 3区 医学 Q3 ONCOLOGY
Christoph Paul Klapproth , Felix Fischer , Annika Doehmen , Milan Kock , Jens Rohde , Kathrin Rieger , Ullrich Keilholz , Matthias Rose , Alexander Obbarius
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引用次数: 0

Abstract

Background

The Patient-Reported Outcomes Measurement Information System (PROMIS) Preference Score (PROPr) is estimated from descriptive health assessments within the PROMIS framework. The underlying item response theory (IRT) allows researchers to measure PROMIS health domains with any subset of items that are calibrated to this domain. Consequently, this should also be true for the PROPr. We aimed to test this assumption using both an empirical and a simulation approach.

Methods

Empirically, we estimated 3 PROMIS Pain inference (PI) scores from 3 different item subsets in a sample of n=199 cancer patients: 4 PROMIS-29 items (estimate: θ4), the 2 original PROPr items (θ2), and 10 different items (θ10). We calculated mean differences and agreement between θ4, and θ2 and θ10, respectively, and between their resulting PROPr4, PROPr2, PROPr10, using intraclass correlation coefficients (ICC) and Bland-Altman (B-A) plots with 95 %-Limits of Agreement (LoA). For the simulation, we used the IRT-model to calculate all item responses of the entire 7 PROPr domain item banks from the empirically observed PROMIS-29+cognition θ. From these simulated item banks, we chose the 2 original PROPr items per domain to calculate PROPrsim and compared it to PROPr4 again using ICC and B-A plots.

Results

θ4 vs θ10 showed smaller bias (-0.012, 95 %-LoA −0.88;0.85) than θ4 vs θ2 (0.025, 95 %-LoA −0.95;1.00. ICC>0.85 (p<0.001) in both θ-comparisons. PROPr4 vs PROPr10 showed lower bias (0.0012, 95 %-LoA −0.039;0.042) than PROPr4 vs PROPr2 (-0.0029, 95 %-LoA −0.049;0.044). ICC>0.98 (p<0.0001) on both PROPr-comparisons. Mean PROPrsim was larger than mean PROPr4 (0.0228, 95 %-LoA −0.1103; 0.1558) and ICC was 0.95 (95 %CI 0.93; 0.97).

Conclusion

Different item subsets can be used to estimate the PROMIS PI for calculation of the PROPr. Reduction to 2 items per domain rather than 4 does not significantly change the PROPr estimate on average. Agreements differ across the spectrum and in individual comparisons.

PROPr 可使用不同的 PROMIS 领域项目集进行测量
背景患者报告结果测量信息系统(PROMIS)偏好分数(PROPr)是在 PROMIS 框架内通过描述性健康评估估算出来的。所依据的项目反应理论(IRT)允许研究人员使用校准到该领域的任何项目子集来测量 PROMIS 健康领域。因此,PROPr 也应如此。我们的目标是使用经验和模拟方法来验证这一假设。方法在经验方面,我们从 3 个不同的项目子集中估算出了 3 个 PROMIS 疼痛推断 (PI) 分数,样本为 n=199 癌症患者:4 个 PROMIS-29 项目(估计值:θ4)、2 个原始 PROPr 项目(θ2)和 10 个不同项目(θ10)。我们使用类内相关系数 (ICC) 和带有 95 % 协议限值 (LoA) 的 Bland-Altman (B-A) 图分别计算了θ4、θ2 和θ10 之间的平均差异和一致性,以及它们所产生的 PROPr4、PROPr2 和 PROPr10 之间的平均差异和一致性。在模拟过程中,我们使用 IRT 模型,根据经验观察到的 PROMIS-29+cognition θ 计算整个 7 个 PROPr 领域项目库的所有项目反应。结果θ4 vs θ10显示的偏差(-0.012,95 %-LoA -0.88;0.85)小于θ4 vs θ2(0.025,95 %-LoA -0.95;1.00)。两个 θ 比较的 ICC>0.85 (p<0.001)。PROPr4 与 PROPr10 的偏差(0.0012,95%-LoA -0.039;0.042)低于 PROPr4 与 PROPr2 的偏差(-0.0029,95%-LoA -0.049;0.044)。两个 PROPr 比较的 ICC>0.98 (p<0.0001)。平均 PROPrsim 大于平均 PROPr4 (0.0228, 95 %-LoA -0.1103; 0.1558),ICC 为 0.95 (95 %CI 0.93; 0.97)。将每个领域的项目从 4 个减少到 2 个并不会明显改变 PROPr 的平均估计值。在不同领域和个别比较中的一致性有所不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Cancer Epidemiology
Cancer Epidemiology 医学-肿瘤学
CiteScore
4.50
自引率
3.80%
发文量
200
审稿时长
39 days
期刊介绍: Cancer Epidemiology is dedicated to increasing understanding about cancer causes, prevention and control. The scope of the journal embraces all aspects of cancer epidemiology including: • Descriptive epidemiology • Studies of risk factors for disease initiation, development and prognosis • Screening and early detection • Prevention and control • Methodological issues The journal publishes original research articles (full length and short reports), systematic reviews and meta-analyses, editorials, commentaries and letters to the editor commenting on previously published research.
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