Methods to assess an exercise intervention trial based on 3-level functional data
Methods to assess an exercise intervention trial based on 3-level functional data
复制标题
DOI：
10.1093/biostatistics/kxv015
复制
发表时间：
2015-10-01
期刊：
影响因子：
2.1
通讯作者：
Carroll, Raymond J.
中科院分区：
文献类型：
--
作者：
Li, Haocheng;Keadle, Sarah Kozey;Carroll, Raymond J.
受记录运动干预对受试者身体活动随时间推移的影响的数据的激励，我们开发了一个模型，以评估当数据具有3个水平（受试者，周和天）并且可能不完整时的治疗效果。我们开发了一个具有3水平平均结构效应的模型，所有模型均按治疗和受试者随机效应分层，包括一般受试者效应和3个水平的嵌套效应。将平均值和随机结构指定为在不同时间点测量的平滑曲线。三水平数据的关联结构是通过随机曲线来归纳的，这些随机曲线是用几个重要的主成分来概括的。我们使用惩罚样条来模拟均值曲线和主成分曲线，并将所提出的模型转换为一个混合效应模型框架，用于模型拟合、预测和推理。我们开发了一种算法，以适应模型的期望/条件最大化（ECME）版本的EM算法和特征值分解迭代。算法中考虑了主成分个数的选择和不完全数据的处理问题。Wald型假设检验的性能也进行了讨论。该方法适用于身体活动数据和经验评估的模拟研究。
Motivated by data recording the effects of an exercise intervention on subjects' physical activity over time, we develop a model to assess the effects of a treatment when the data are functional with 3 levels (subjects, weeks and days in our application) and possibly incomplete. We develop a model with 3-level mean structure effects, all stratified by treatment and subject random effects, including a general subject effect and nested effects for the 3 levels. The mean and random structures are specified as smooth curves measured at various time points. The association structure of the 3-level data is induced through the random curves, which are summarized using a few important principal components. We use penalized splines to model the mean curves and the principal component curves, and cast the proposed model into a mixed effects model framework for model fitting, prediction and inference. We develop an algorithm to fit the model iteratively with the Expectation/Conditional Maximization Either (ECME) version of the EM algorithm and eigenvalue decompositions. Selection of the number of principal components and handling incomplete data issues are incorporated into the algorithm. The performance of the Wald-type hypothesis test is also discussed. The method is applied to the physical activity data and evaluated empirically by a simulation study.