Power analyses for longitudinal study designs with missing data
Power analyses for longitudinal study designs with missing data
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DOI：
10.1002/sim.2773
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发表时间：
2007-07-10
影响因子：
2
通讯作者：
Tang, W.
中科院分区：
文献类型：
--
作者：
Tu, X. M.;Zhang, J.;Tang, W.
纵向研究设计的功效分析的现有方法是有限的，因为它们不能充分解决随机缺失数据模式。虽然可以在数据分析期间评估缺失数据的模式，但在研究的设计阶段是未知的。缺失数据模式的随机性质在解决缺失数据以进行功效分析时增加了另一层复杂性。在本文中，我们用一个两状态的一阶马尔可夫过程来模拟缺失数据的发生，并将建模信息集成到幂函数中，以解释随机缺失数据模式。马尔可夫模型很容易指定，以适应不同的预期缺失数据过程。我们开发了这种方法的两个最流行的纵向模型：广义估计方程（GEE）和线性混合效应模型下完全随机缺失（MCAR）的假设。对于GEE，我们也限制我们的考虑工作独立性相关模型。所提出的方法与众多的例子，是出于真实的研究设计。版权所有（c）2006约翰威利父子有限公司。
Existing methods for power analysis for longitudinal study designs are limited in that they do not adequately address random missing data patterns. Although the pattern of missing data can be assessed during data analysis, it is unknown during the design phase of a study. The random nature of the missing data pattern adds another layer of complexity in addressing missing data for power analysis. In this paper, we model the occurrence of missing data with a two-state, first-order Markov process and integrate the modelling information into the power function to account for random missing data patterns. The Markov model is easily specified to accommodate different anticipated missing data processes. We develop this approach for the two most popular longitudinal models: the generalized estimating equations (GEE) and the linear mixed-effects model under the missing completely at random (MCAR) assumption. For GEE, we also limit our consideration to the working independence correlation model. The proposed methodology is illustrated with numerous examples that are motivated by real study designs. Copyright (c) 2006 John Wiley & Sons, Ltd.