Vonesh Edward F, Greene Tom, Schluchter Mark D
Baxter Healthcare Corporation, Round Lake, IL 60073, USA.
Stat Med. 2006 Jan 15;25(1):143-63. doi: 10.1002/sim.2249.
Longitudinal studies often gather joint information on time to some event (survival analysis, time to dropout) and serial outcome measures (repeated measures, growth curves). Depending on the purpose of the study, one may wish to estimate and compare serial trends over time while accounting for possibly non-ignorable dropout or one may wish to investigate any associations that may exist between the event time of interest and various longitudinal trends. In this paper, we consider a class of random-effects models known as shared parameter models that are particularly useful for jointly analysing such data; namely repeated measurements and event time data. Specific attention will be given to the longitudinal setting where the primary goal is to estimate and compare serial trends over time while adjusting for possible informative censoring due to patient dropout. Parametric and semi-parametric survival models for event times together with generalized linear or non-linear mixed-effects models for repeated measurements are proposed for jointly modelling serial outcome measures and event times. Methods of estimation are based on a generalized non-linear mixed-effects model that may be easily implemented using existing software. This approach allows for flexible modelling of both the distribution of event times and of the relationship of the longitudinal response variable to the event time of interest. The model and methods are illustrated using data from a multi-centre study of the effects of diet and blood pressure control on progression of renal disease, the modification of diet in renal disease study.
纵向研究通常会收集关于某个事件发生时间（生存分析、失访时间）的联合信息以及系列结局测量指标（重复测量、生长曲线）。根据研究目的，研究者可能希望在考虑可能不可忽略的失访情况下估计和比较随时间的系列趋势，或者希望研究感兴趣的事件时间与各种纵向趋势之间可能存在的关联。在本文中，我们考虑一类称为共享参数模型的随机效应模型，这类模型对于联合分析此类数据特别有用，即重复测量数据和事件时间数据。我们将特别关注纵向研究场景，其主要目标是在调整因患者失访导致的可能信息删失的情况下，估计和比较随时间的系列趋势。本文提出了用于事件时间的参数和半参数生存模型，以及用于重复测量的广义线性或非线性混合效应模型，以联合建模系列结局测量指标和事件时间。估计方法基于一个广义非线性混合效应模型，该模型可使用现有软件轻松实现。这种方法允许对事件时间的分布以及纵向响应变量与感兴趣的事件时间之间的关系进行灵活建模。我们使用来自一项关于饮食和血压控制对肾脏疾病进展影响的多中心研究（肾脏疾病饮食改良研究）的数据来说明该模型和方法。
