Müller Peter, Quintana Fernando A, Rosner Gary L, Maitland Michael L
Department of Mathematics, University of Texas at Austin, Austin, TX 78712, USA.
Biostatistics. 2014 Apr;15(2):341-52. doi: 10.1093/biostatistics/kxt049. Epub 2013 Nov 26.
We consider inference for longitudinal data based on mixed-effects models with a non-parametric Bayesian prior on the treatment effect. The proposed non-parametric Bayesian prior is a random partition model with a regression on patient-specific covariates. The main feature and motivation for the proposed model is the use of covariates with a mix of different data formats and possibly high-order interactions in the regression. The regression is not explicitly parameterized. It is implied by the random clustering of subjects. The motivating application is a study of the effect of an anticancer drug on a patient's blood pressure. The study involves blood pressure measurements taken periodically over several 24-h periods for 54 patients. The 24-h periods for each patient include a pretreatment period and several occasions after the start of therapy.
我们考虑基于混合效应模型对纵向数据进行推断，该模型对治疗效果采用非参数贝叶斯先验。所提出的非参数贝叶斯先验是一种随机划分模型，对患者特定协变量进行回归。所提出模型的主要特征和动机是在回归中使用具有不同数据格式混合以及可能存在高阶交互作用的协变量。该回归没有明确的参数化形式。它由受试者的随机聚类隐含表示。其激励性应用是一项关于抗癌药物对患者血压影响的研究。该研究涉及对54名患者在几个24小时时间段内定期进行的血压测量。每个患者的24小时时间段包括一个预处理期和治疗开始后的若干次测量。
