Rosner B, Muñoz A, Tager I, Speizer F, Weiss S
Stat Med. 1985 Oct-Dec;4(4):457-67. doi: 10.1002/sim.4780040407.
Korn and Whittemore have presented methods for analyzing longitudinal data where the number of observations per individual is large relative to the number of variables considered for each subject. However, this is often not the case in epidemiologic studies, since one usually collects data at relatively few time points, and the quantity of data collected for each individual at each time point is typically extensive. We present here an autoregressive model for analyzing longitudinal data of this type for the case of a continuous outcome variable. Some of the important features of this model are that one can in the same analysis, consider both independent variables that are time-dependent and those that are fixed over time, partially use data for an individual where some examinations are missing, assess relationships between changes in outcome and exposure over short periods of time, use ordinary multiple regression methods. Anderson has considered this type of model, but, to our knowledge, the model has never been applied to biostatistical problems. We illustrate these methods with data from a longitudinal study that seeks to identify the role of personal cigarette smoking on changes in pulmonary function in children.
科恩和惠特莫尔提出了分析纵向数据的方法，在这类方法中，相对于为每个受试者考虑的变量数量而言，每个个体的观察数量较多。然而，在流行病学研究中情况往往并非如此，因为通常在相对较少的时间点收集数据，而且在每个时间点为每个个体收集的数据量通常很大。在此，我们针对连续结局变量的情况，提出一种用于分析此类纵向数据的自回归模型。该模型的一些重要特征包括：在同一分析中，既可以考虑随时间变化的自变量，也可以考虑随时间固定的自变量；对于某些检查缺失的个体，可以部分使用其数据；评估短时间内结局变化与暴露之间的关系；使用普通多元回归方法。安德森曾考虑过这类模型，但据我们所知，该模型从未应用于生物统计学问题。我们用一项纵向研究的数据来说明这些方法，该研究旨在确定个人吸烟对儿童肺功能变化的作用。
