Follmann D, Wu M
Office of Biostatistics Research, National Heart, Lung, and Blood Institute, Bethesda, Maryland 20892, USA.
Biometrics. 1995 Mar;51(1):151-68.
This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are linked by a common random parameter. Such models have been developed in the econometrics (Heckman, 1979, Econometrica 47, 153-161) and biostatistics (Wu and Carroll, 1988, Biometrics 44, 175-188) literature for a Gaussian primary response. We allow the primary response, conditional on the random parameter, to follow a generalized linear model and approximate the generalized linear model by conditioning on the data that describes missingness. The resultant approximation is a mixed generalized linear model with possibly heterogeneous random effects. An example is given to illustrate the approximate approach, and simulations are performed to critique the adequacy of the approximation for repeated binary data.
本文开发了一类模型来处理纵向研究中的缺失数据。我们假设用于主要反应和缺失情况（例如，错过的访视次数）的单独模型通过一个共同的随机参数联系起来。此类模型已在计量经济学（赫克曼，1979年，《计量经济学》第47卷，第153 - 161页）和生物统计学（吴和卡罗尔，1988年，《生物统计学》第44卷，第175 - 188页）文献中针对高斯主要反应进行了开发。我们允许在随机参数条件下的主要反应遵循广义线性模型，并通过对描述缺失情况的数据进行条件设定来近似广义线性模型。由此得到的近似是一个可能具有异质随机效应的混合广义线性模型。给出了一个例子来说明这种近似方法，并进行了模拟以评估该近似对于重复二元数据的充分性。
