对集群随机对照试验的治疗效果的模型变异性,使用由变量平滑函数在一般化添加式混合模型中.
Sun-Joo Cho1, Kristopher J Preacher2, Haley E Yaremych2
1Vanderbilt University, Nashville, TN, USA. sj.cho@vanderbilt.edu.
Behavior research methods
|August 9, 2023
概括
一般化添加混合模型 (GAMM) 为分析集群随机对照试验 (C-RCTs) 中的治疗-共变相互作用提供了多层模型 (MLM) 的灵活替代方案. GAMM准确地捕捉了非线性关系,改善了对治疗效应的统计推断.
科学领域:
- 统计 统计 统计 统计
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 集群随机对照试验 (C-RCTs) 容易受到治疗效果的变化.
- 多级建模 (MLM) 常用于分析C-RCTs中的治疗-共变相互作用 (TRT COV).
- 多元市场假设共变量和结果之间的线性关系,如果违反,可能会导致有偏见的推断.
研究的目的:
- 引入通用添加混合模型 (GAMM) 用于在C-RCT中建模非线性集群特定的共变量-结果关系.
- 通过使用mgcv R包来展示GAMM的实现.
- 评估GAMM作为MLM的替代品,用于分析治疗-共变相互作用.
主要方法:
- 开发了用于集群特定关系的由变量流函数的GAMM规范.
- 在R中使用mgcv R包来实现GAMM.
- 进行模拟研究,并将模型应用于C-RCT干预数据.
主要成果:
- 在多层设计中,GAMM成功地恢复了参数和变量顺函数.
- 在MLM中对线性关系的错误规范导致对TRT COV效应的偏见估计.
- GAMM证明了作为MLM的替代品的潜力,即使与线性关系.
结论:
- 通过容纳非线性关系,GAMM提供了一种强大的方法来分析C-RCTs中的治疗-共变相互作用.
- 这种方法提高了统计推断的准确性,与传统的MLM相比,当不符合线性假设时.
- 对于研究人员来说,GAMM为研究集群数据中复杂的治疗效果变异提供了有价值的工具.
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