Palta M, Yao T J, Velu R
Department of Preventive Medicine, University of Wisconsin, Madison 53705.
Stat Med. 1994 Nov 15;13(21):2219-31. doi: 10.1002/sim.4780132104.
When fitting regression models to investigate the relationship between an outcome variable and independent variables of primary interest, there is often concern whether omitted variables or assuming a different functional relationship could have changed the conclusion or interpretation of the results. In longitudinal studies of aging, the concern with omitted variables is well known in the context of cohort and period effects, which refer to unmeasured variables systematically related to the individual's year of birth and secular trends in outcome, respectively. We present and compare three approaches to detecting omitted confounders and non-linearity in the random effects model for longitudinal data (Laird and Ware, 1982) with random slope and intercept across individuals. The first approach compares simple unweighted within and between regression coefficients, the second is the Hausman specification test for regression models, and the third approach involves testing directly the significance of functions of individual specific covariate means means i, in the random effects regression model. This last approach is motivated by the models that arise when cohort or period effects are ignored. We compare the three approaches, and illustrate their application.
在拟合回归模型以研究结果变量与主要关注的自变量之间的关系时，人们常常担心遗漏变量或假设不同的函数关系是否会改变结果的结论或解释。在衰老的纵向研究中，在队列效应和时期效应的背景下，遗漏变量的问题是众所周知的，队列效应和时期效应分别指与个体出生年份系统相关的未测量变量和结果中的长期趋势。我们提出并比较了三种方法，用于在具有个体间随机斜率和截距的纵向数据随机效应模型（Laird和Ware，1982）中检测遗漏的混杂因素和非线性。第一种方法比较简单的未加权组内和组间回归系数，第二种是回归模型的豪斯曼设定检验，第三种方法涉及在随机效应回归模型中直接检验个体特定协变量均值函数的显著性。最后一种方法的动机来自于忽略队列效应或时期效应时出现的模型。我们比较这三种方法，并说明它们的应用。
