Dataset accompanying PhD thesis: Who benefits most? Evaluating and understanding clinical and biomechanical outcomes following structured education and exercise therapy interventions for people with knee osteoarthritis

This statistical code was generated for the data analysis of three research studies as part of a PhD thesis titled: Who benefits most? Evaluating and understanding clinical and biomechanical outcomes following structured education and exercise therapy interventions for people with knee osteoarthritis.

Study 2 (Chapter 5) was a methods paper titled " A statistical model of agreement in subjective rating scales—an exploration of the Kellgren-Lawrence radiological grading system." This paper outlines a workflow for a statistical modelling approach for defining radiological knee OA severity and rater agreement from the Kellgren-Lawrence (KL) system. The analysis utilises the cumulative-link model as implemented in 'brms' (https://doi.org/10.1177/2515245918823199). The data generated were used in study 3 and 4.

Study 3 (Chapter 6) was a clinical outcomes study titled " The relationship between radiological OA severity or body weight and outcomes following a structured education and exercise therapy program (GLA:D®) for people with knee osteoarthritis." This pre-post study of 33 participants with knee OA evaluated the relationship between a person's body weight or radiological knee compartment severity and short-term outcomes following the GLA:D® program. The data and workflow for this study have been provided which includes the R code for all models and graphics.

Study 4 (Chapter 7) was a biomechanical study titled " Knee joint moment changes during walking and chair-rise and the relationship to radiological knee OA severity and body weight following a structured education and exercise intervention (GLA:D®) for knee osteoarthritis". This pre-post study of 31 participants with knee OA evaluated knee joint moment changes during walking and chair-rise and the relationship to radiological knee OA severity and body weight following the GLA:D® intervention. The documents provided includes the preprocessing workflow that imports the original csv files generated from VICON and the code to generate the secondary parameters (such as peak values and total areas under the curve).

此统计代码旨在对三项研究的数据进行分析，三项研究作为博士论文《谁受益最大？评估和理解结构性教育和运动疗法干预对膝关节骨关节炎患者临床和生物力学结果的影响》的一部分。研究二（第五章）为一篇关于方法的论文，题为《主观评分量表一致性统计模型——对Kellgren-Lawrence放射学分级系统的探索》。该论文概述了利用Kellgren-Lawrence（KL）系统定义放射学膝关节骨关节炎严重程度和评分者一致性统计建模方法的流程。分析使用了累积链接模型，该模型由brms（https://doi.org/10.1177/2515245918823199）实现。所生成数据被用于研究三和研究四。研究三（第六章）为一项临床结果研究，题为《放射学骨关节炎严重程度或体重与膝关节骨关节炎患者接受结构性教育和运动疗法项目（GLA:D®）后短期结果的关系》。该对33名膝关节骨关节炎患者进行的预后研究评估了患者的体重或放射学膝关节腔室严重程度与GLA:D®项目短期结果之间的关系。该研究提供了数据和工作流程，包括所有模型的R代码和图形。研究四（第七章）为一项生物力学研究，题为《步行和椅起过程中膝关节关节力矩变化及其与结构性教育和运动干预（GLA:D®）后放射学膝关节骨关节炎严重程度和体重的关系》。该对31名膝关节骨关节炎患者进行的预后研究评估了步行和椅起过程中膝关节关节力矩变化及其与放射学膝关节骨关节炎严重程度和体重的关系。提供的相关文档包括从VICON生成的原始csv文件的预处理工作流程以及生成次级参数（如峰值值和曲线下总面积）的代码。