两个样本的门德尔随机化,使用疾病进展的多层模型的结果
Michael Lawton1, Yoav Ben-Shlomo2, Apostolos Gkatzionis2,3
1Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK. Michael.Lawton@bristol.ac.uk.
European journal of epidemiology
|January 28, 2024
概括
这项研究引入了对孟德尔随机化的多变量方法,用于分析疾病进展轨迹. 该方法的性能与单变量方法相似,但更好地覆盖了疾病严重程度和进展率的联合覆盖.
科学领域:
- 流行病学 流行病学
- 遗传学 是一个遗传学.
- 生物统计学 生物统计学
背景情况:
- 鉴定疾病进展的因果因素,特别是神经退行性疾病,至关重要.
- 疾病的进展通常被建模为线性轨迹,其中有截止点 (初始严重程度) 和斜率 (变化速率).
- 双样本门德尔随机化 (2SMR) 是一种从观测数据中推断因果关系的方法,减轻了混偏差.
研究的目的:
- 开发和评估一种多变量双样本门德尔随机化 (2SMR) 方法来分析疾病进展.
- 估计暴露对线性疾病进展轨迹的交叉点和斜率的因果影响.
- 为了比较多变量2SMR方法与单变量2SMR方法的性能.
主要方法:
- 使用多层次疾病进展模型开发了一种多变量2SMR方法.
- 进行了一项模拟研究,将多变量2SMR方法与单变量2SMR方法进行比较.
- 模拟涉及的场景暴露影响线性渐进的结果的拦截和斜率.
- 这些方法应用于两个帕金森病队列,以评估体重指数 (BMI) 对疾病进展的影响.
主要成果:
- 模拟结果表明,单变量和多变量2SMR方法都没有显著的证据证明非零偏差.
- 对拦截 (93.4-96.2%) 和斜率 (94.5-96.0%) 的置信区间覆盖在模拟中是适当的.
- 多变量方法为拦截和斜率效应提供了更好的联合覆盖.
- 对帕金森氏症队伍的分析没有发现强有力的证据表明BMI因果影响疾病进展,尽管置信区间很宽.
结论:
- 开发的多变量2SMR方法是估计疾病进展轨迹因果影响的可行方法.
- 虽然模拟结果与单变量方法可比,但多变量方法提供了更好的进展参数的联合估计.
- 需要对更大的队列进行进一步的研究,以确认BMI对帕金森病进展的影响,因为信任区间很宽.
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