Tsonaka Roula, Rizopoulos Dimitris, Verbeke Geert, Lesaffre Emmanuel
Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Katholieke Universiteit Leuven, Leuven, Belgium.
Biometrics. 2010 Sep;66(3):834-44. doi: 10.1111/j.1541-0420.2009.01365.x.
A class of nonignorable models is presented for handling nonmonotone missingness in categorical longitudinal responses. This class of models includes the traditional selection models and shared parameter models. This allows us to perform a broader than usual sensitivity analysis. In particular, instead of considering variations to a chosen nonignorable model, we study sensitivity between different missing data frameworks. An appealing feature of the developed class is that parameters with a marginal interpretation are obtained, while algebraically simple models are considered. Specifically, marginalized mixed-effects models (Heagerty, 1999, Biometrics 55, 688-698) are used for the longitudinal process that model separately the marginal mean and the correlation structure. For the correlation structure, random effects are introduced and their distribution is modeled either parametrically or non-parametrically to avoid potential misspecifications.
提出了一类非ignorable模型，用于处理分类纵向响应中的非单调缺失。这类模型包括传统的选择模型和共享参数模型。这使我们能够进行比通常更广泛的敏感性分析。特别是，我们不是考虑对选定的非ignorable模型进行变化，而是研究不同缺失数据框架之间的敏感性。所开发的这类模型的一个吸引人的特点是，在考虑代数上简单的模型时，能够获得具有边际解释的参数。具体来说，边际化混合效应模型（Heagerty，1999年，《生物统计学》55卷，688 - 698页）用于纵向过程，该模型分别对边际均值和相关结构进行建模。对于相关结构，引入随机效应，并对其分布进行参数化或非参数化建模，以避免潜在的模型误设。
