Measuring the individual benefit of a medical or behavioral treatment using generalized linear mixed-effects models

A statistical measure of the individual benefit of medical or behavioral treatment and of the severity of a chronic illness is proposed, which are used to develop a graphical method that can be used by statisticians and clinicians in the data analysis of clinical trials from the perspective of personalized medicine.
The method focuses on assessing and comparing individual effects of treatments rather than average effects and can be used with continuous and discrete responses under generalized linear mixed-effects models framework. Analyses of data from the Sequenced Treatment Alternatives to Relieve Depression clinical trial of sequences of treatments for depression and data from a clinical trial of respiratory treatments are used for illustration.

本研究提出了一种可同时衡量药物或行为治疗个体获益与慢性疾病严重程度的统计指标，并基于该指标开发了一套可视化分析方法，可支持统计学家与临床医师从个性化医疗（personalized medicine）视角开展临床试验数据分析工作。
该方法聚焦于评估与比较治疗的个体效应而非群体平均效应，可在广义线性混合效应模型（generalized linear mixed-effects models）框架下处理连续型与离散型响应变量。
本研究采用针对抑郁症的序贯治疗替代方案缓解抑郁症（Sequenced Treatment Alternatives to Relieve Depression）临床试验数据集以及呼吸治疗相关临床试验数据集，对所提出的方法进行了实例演示。