Yang Xiaowei, Li Jinhui, Shoptaw Steven
Division of Biostatistics, School of Medicine, University of California, Med Sci 1-C, Suite 200, Davis, CA 95616, USA.
Stat Med. 2008 Jul 10;27(15):2826-49. doi: 10.1002/sim.3111.
Biomedical research is plagued with problems of missing data, especially in clinical trials of medical and behavioral therapies adopting longitudinal design. After a literature review on modeling incomplete longitudinal data based on full-likelihood functions, this paper proposes a set of imputation-based strategies for implementing selection, pattern-mixture, and shared-parameter models for handling intermittent missing values and dropouts that are potentially nonignorable according to various criteria. Within the framework of multiple partial imputation, intermittent missing values are first imputed several times; then, each partially imputed data set is analyzed to deal with dropouts with or without further imputation. Depending on the choice of imputation model or measurement model, there exist various strategies that can be jointly applied to the same set of data to study the effect of treatment or intervention from multi-faceted perspectives. For illustration, the strategies were applied to a data set with continuous repeated measures from a smoking cessation clinical trial.
生物医学研究饱受数据缺失问题的困扰，尤其是在采用纵向设计的医学和行为疗法的临床试验中。在对基于全似然函数的不完全纵向数据建模进行文献综述之后，本文提出了一套基于插补的策略，用于实施选择模型、模式混合模型和共享参数模型，以处理根据各种标准可能不可忽略的间歇性缺失值和失访情况。在多重多重插补的框架内，首先对间歇性缺失值进行多次插补；然后，对每个部分插补后的数据集进行分析，以处理有无进一步插补的失访情况。根据插补模型或测量模型的选择，存在各种策略，这些策略可以联合应用于同一组数据，以便从多方面研究治疗或干预的效果。为了说明，这些策略被应用于一个来自戒烟临床试验的具有连续重复测量的数据集。
