Albert Paul S, Follmann Dean A
Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, USA.
Stat Methods Med Res. 2007 Oct;16(5):417-39. doi: 10.1177/0962280206075308. Epub 2007 Jul 26.
The analysis of longitudinal data with non-ignorable missingness remains an active area in biostatistics research. This article discusses various random effects and latent process models which have been proposed for analyzing longitudinal binary data subject to both non-ignorable intermittent missing data and dropout. These models account for non-ignorable missingness by introducing random effects or a latent process which is shared between the response model and the model for the missing-data mechanism. We discuss various random effects and latent processes approaches and compare these approaches with analyses from an opiate clinical trial data set, which had high proportion of intermittent missingness and dropout. We also compare these random effect and latent process approaches with other methods for accounting for non-ignorable missingness using this data set.
对具有不可忽略缺失值的纵向数据进行分析仍是生物统计学研究中的一个活跃领域。本文讨论了各种随机效应和潜在过程模型，这些模型已被提出用于分析纵向二元数据，该数据同时存在不可忽略的间歇性缺失数据和失访情况。这些模型通过引入随机效应或潜在过程来考虑不可忽略的缺失值，该潜在过程在响应模型和缺失数据机制模型之间共享。我们讨论了各种随机效应和潜在过程方法，并将这些方法与来自阿片类药物临床试验数据集的分析进行比较，该数据集具有较高比例的间歇性缺失值和失访情况。我们还使用该数据集将这些随机效应和潜在过程方法与其他考虑不可忽略缺失值的方法进行比较。
