患者可行性是将 IRT 和 LCA 统计模型整合到以患者为中心的定性数据中的一种新方法--试点研究。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2024-10-02 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1378497
Matthias Klüglich, Bert Santy, Mihail Tanev, Kristian Hristov, Tsveta Mincheva
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引用次数: 0

摘要

引言:临床研究日益认识到在试验设计中纳入以患者为中心的数据的作用和价值,旨在为参与研究的患者提供更相关、更可行、更有吸引力的研究。目的:本试验研究旨在探索和展示 "患者可行性 "概念的分析框架--一种利用心理测量潜类分析(LCA)和区间反应理论(IRT)模型将以患者为中心的数据纳入临床试验设计的新方法:设计了一项定性调查,以了解肿瘤适应症患者的不同经历和态度。作为研究的准备阶段,对调查结果进行了内容分析和分类。分析阶段进一步采用 LCA 和混合 IRT 模型来识别与患者可行性相关的不同患者亚群和特征:LCA确定了三个潜在类别,每个类别都具有与患者可行性定义的潜在特质相关的独特特征。协变量分析进一步突出了亚组行为。此外,使用双参数逻辑模型、广义部分信用模型和名义反应模型进行的IRT分析进一步突出了所研究群体的不同特征。研究结果深入揭示了治疗过程中存在的挑战、后勤挑战以及标准护理疗法和临床试验态度方面的限制因素。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Patient feasibility as a novel approach for integrating IRT and LCA statistical models into patient-centric qualitative data-a pilot study.

Introduction: Clinical research increasingly recognizes the role and value of patient-centric data incorporation in trial design, aiming for more relevant, feasible, and engaging studies for participating patients. Despite recognition, research on analytical models regarding qualitative patient data analysis has been insufficient.

Aim: This pilot study aims to explore and demonstrate the analytical framework of the "patient feasibility" concept-a novel approach for integrating patient-centric data into clinical trial design using psychometric latent class analysis (LCA) and interval response theory (IRT) models.

Methods: A qualitative survey was designed to capture the diverse experiences and attitudes of patients in an oncological indication. Results were subjected to content analysis and categorization as a preparatory phase of the study. The analytical phase further employed LCA and hybrid IRT models to discern distinct patient subgroups and characteristics related to patient feasibility.

Results: LCA identified three latent classes each with distinct characteristics pertaining to a latent trait defined as patient feasibility. Covariate analyses further highlighted subgroup behaviors. In addition, IRT analyses using the two-parameter logistic model, generalized partial credit model, and nominal response model highlighted further distinct characteristics of the studied group. The results provided insights into perceived treatment challenges, logistic challenges, and limiting factors regarding the standard of care therapy and clinical trial attitudes.

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CiteScore
4.20
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