Hammas Karima, Sébille Véronique, Brisson Priscilla, Hardouin Jean-Benoit, Blanchin Myriam
U1246 SPHERE "methodS in Patient centered outcomes and HEalth ResEarch", Université de Nantes, Université de Tours, INSERM, Nantes, France.
Methodology and Biostatistics Unit, CHU of Nantes, Nantes, France.
Front Psychol. 2020 Dec 23;11:613482. doi: 10.3389/fpsyg.2020.613482. eCollection 2020.
In order to investigate patients' experience of healthcare, repeated assessments of patient-reported outcomes (PRO) are increasingly performed in observational studies and clinical trials. Changes in PRO can however be difficult to interpret in longitudinal settings as patients' perception of the concept being measured may change over time, leading to response shift (longitudinal measurement non-invariance) and possibly to erroneous interpretation of the observed changes in PRO. Several statistical methods for response shift analysis have been proposed, but they usually assume that response shift occurs in the same way in all individuals within the sample regardless of their characteristics. Many studies aim at comparing the longitudinal change of PRO into two groups of patients (treatment arm, different pathologies, …). The group variable could have an effect on PRO change but also on response shift effect and the perception of the questionnaire at baseline. In this paper, we propose to enhance the ROSALI algorithm based on Rasch Measurement Theory for the analysis of longitudinal PRO data to simultaneously investigate the effects of group on item functioning at the first measurement occasion, on response shift and on changes in PRO over time. ROSALI is subsequently applied to a longitudinal dataset on change in emotional functioning in patients with breast cancer or melanoma during the year following diagnosis. The use of ROSALI provides new insights in the analysis of longitudinal PRO data.
为了调查患者的医疗保健体验，在观察性研究和临床试验中越来越多地对患者报告结局（PRO）进行重复评估。然而，在纵向研究中，PRO的变化可能难以解释，因为患者对所测量概念的认知可能随时间而改变，从而导致反应转移（纵向测量不恒定），并可能导致对观察到的PRO变化的错误解释。已经提出了几种用于反应转移分析的统计方法，但它们通常假设样本中的所有个体无论其特征如何，反应转移的发生方式都是相同的。许多研究旨在比较两组患者（治疗组、不同病理类型等）PRO的纵向变化。组变量可能对PRO变化有影响，但也可能对反应转移效应和基线时问卷的认知有影响。在本文中，我们建议基于拉施测量理论增强ROSALI算法，用于纵向PRO数据分析，以同时研究组在首次测量时对项目功能、反应转移以及PRO随时间变化的影响。随后将ROSALI应用于一个关于乳腺癌或黑色素瘤患者在诊断后一年内情绪功能变化的纵向数据集。ROSALI的使用为纵向PRO数据分析提供了新的见解。
