使用患者报告的结果测量的定量研究的反应转移结果:荟萃回归分析。

IF 3.3 3区 医学 Q1 HEALTH CARE SCIENCES & SERVICES
Quality of Life Research Pub Date : 2025-05-01 Epub Date: 2024-12-09 DOI:10.1007/s11136-024-03867-x
Richard Sawatzky, Mathilde G E Verdam, Yseulys Dubuy, Tolulope T Sajobi, Lara Russell, Oluwagbohunmi A Awosoga, Ayoola Ademola, Jan R Böhnke, Oluwaseyi Lawal, Anita Brobbey, Amélie Anota, Lisa M Lix, Mirjam A G Sprangers, Véronique Sébille
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引用次数: 0

摘要

目的:我们的目的是利用患者报告的结果(PROMs)来确定反应转移研究的特征,这些特征可以解释(1)反应转移效应的检测和(2)反应转移效应的大小的变异性。方法:我们对2023年6月前发表的定量研究进行了系统综述。首先,使用两级多变量逻辑回归模型(效应和样本水平)来解释发现响应移位效应的概率的可变性。其次,采用三水平元回归模型(参与者水平、效应水平和样本水平)研究效应大小(标准化平均差异)的可变性。通过有目的的选择方法确定的解释变量包括反应转移方法和类型,以及人口、研究设计、PROM和研究质量特征。结果:首先,对171项研究中206个样本的5597个效应进行logistic回归分析,确定了解释41.5%效应水平方差的变量,而没有变量解释样本水平方差。响应移位检测的平均概率为0.20 (95% CI: 0.17-0.28)。检测的变化主要由响应移位方法和类型(重新校准vs.重新确定优先级/重新概念化)来解释。其次,采用随机检验和结构方程建模方法,对114个样本和96个研究的769个效应进行效应量分析。meta回归分析确定的变量解释11.6%的效应水平方差和26.4%的样本水平方差,平均效应大小为0.30 (95% CI: 0.26-0.34)。结论:响应位移检测受研究设计和方法的影响。对解释反应转移效应的变量的洞察可以用来解释其他使用prom的可比研究的结果,并为未来反应转移研究的设计提供信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Response shift results of quantitative research using patient-reported outcome measures: a meta-regression analysis.

Purpose: Our objectives were to identify characteristics of response shift studies using patient-reported outcomes (PROMs) that explain variability in (1) the detection and (2) the magnitude of response shift effects.

Methods: We conducted a systematic review of quantitative studies published before June 2023. First, two-level multivariable logistic regression models (effect- and sample-levels) were used to explain variability in the probability of finding a response shift effect. Second, variability in effect sizes (standardized mean differences) was investigated with 3-level meta-regression models (participant-, effect- and sample-levels). Explanatory variables identified via the purposeful selection methodology included response shift method and type, and population-, study design-, PROM- and study-quality characteristics.

Results: First, logistic regression analysis of 5597 effects from 206 samples in 171 studies identified variables explaining 41.5% of the effect-level variance, while no variables explained sample-level variance. The average probability of response shift detection is 0.20 (95% CI: 0.17-0.28). Variation in detection was predominantly explained by response shift methods and type (recalibration vs. reprioritization/reconceptualization). Second, effect sizes were analyzed for 769 effects from 114 samples and 96 studies based on the then-test and structural equation modeling methods. Meta-regression analysis identified variables explaining 11.6% of the effect-level variance and 26.4% of the sample-level variance, with an average effect size of 0.30 (95% CI: 0.26-0.34).

Conclusion: Response shift detection is influenced by study design and methods. Insights into the variables explaining response shift effects can be used to interpret results of other comparable studies using PROMs and inform the design of future response shift studies.

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来源期刊
Quality of Life Research
Quality of Life Research 医学-公共卫生、环境卫生与职业卫生
CiteScore
6.50
自引率
8.60%
发文量
224
审稿时长
3-8 weeks
期刊介绍: Quality of Life Research is an international, multidisciplinary journal devoted to the rapid communication of original research, theoretical articles and methodological reports related to the field of quality of life, in all the health sciences. The journal also offers editorials, literature, book and software reviews, correspondence and abstracts of conferences. Quality of life has become a prominent issue in biometry, philosophy, social science, clinical medicine, health services and outcomes research. The journal''s scope reflects the wide application of quality of life assessment and research in the biological and social sciences. All original work is subject to peer review for originality, scientific quality and relevance to a broad readership. This is an official journal of the International Society of Quality of Life Research.
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