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在试验中的时代级加速度计数据的多重归算方法.

Mia S Tackney1, Elizabeth Williamson1, Derek G Cook2

  • 1Department of Medical Statistics, London School of Hygiene and Tropical Medicine, UK.

Statistical methods in medical research
|July 31, 2023
PubMed
概括
此摘要是机器生成的。

这项研究引入了一种新方法,用于处理体力活动试验中缺少的加速度计数据. 使用多重归算的非参数方法被证明是最有效的,减少了对治疗效果估计的偏差.

关键词:
缺少的数据数据.加速度计的加速度计.多重的归算是多重的归算.身体活动试验试验试验.可穿戴设备可以穿戴.

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科学领域:

  • 生物医学工程 生物医学工程
  • 临床研究方法论 临床研究方法论
  • 数字健康数字健康

背景情况:

  • 加速度计对于测量临床试验中的身体活动至关重要,收集细粒度时代的数据.
  • 缺少非磨损时间的数据是一个常见的挑战,通常通过日级聚合处理,失去时间信息.
  • 现有的缺失数据分析方法可能无法完全捕捉到时代级缺失的细微差别.

研究的目的:

  • 开发和评估新的方法来识别和分类缺少的加速计数据在时代层面.
  • 为了比较参数和非参数的多重归算技术来处理缺失的时代级数据.
  • 评估不同归算策略在体育活动干预中估计治疗效果的表现.

主要方法:

  • 提出了一个时代级的方法来识别和分类缺失的加速度计数据,超越日级的定义.
  • 实施了两个多重归算策略:考虑每天缺失的时代的参数方法和使用捐赠者数据的非参数方法.
  • 进行模拟研究,以比较归算方法的偏差和精度.
  • 将这些方法应用于2017年PACE-UP试验中的真实数据.

主要成果:

  • 非参数的多重归算方法在治疗效果估计中显示出最少的偏差.
  • 这种方法还保持了小的标准误差,表明了良好的精度.
  • 拟议的框架有效地处理可穿戴设备研究中缺少的数据.

结论:

  • 对失踪的加速度计数据的时代级分析提供了比日级聚合更细致的理解.
  • 非参数的多重归算是解决身体活动研究中缺失数据的强有力的方法.
  • 开发的框架可以适应各种数字健康结果和可穿戴传感器.