Testing for omitted variables and non-linearity in regression models for longitudinal data.
Testing for omitted variables and non-linearity in regression models for longitudinal data.
复制标题
测试纵向数据回归模型中的遗漏变量和非线性。
DOI：
10.1002/sim.4780132104
复制
发表时间：
1994
影响因子：
2
通讯作者：
Velu,R
中科院分区：
文献类型：
--
作者：
Palta,M;Yao,TJ;Velu,R
当拟合回归模型以调查结果变量与主要感兴趣的自变量之间的关系时，通常会考虑忽略变量或假设不同的函数关系是否会改变结论或结果的解释。在老龄化的纵向研究中，在队列效应和时期效应的背景下，对遗漏变量的关注是众所周知的，这是指分别与个人出生年份和长期结果趋势系统相关的未测量变量。我们提出并比较了三种方法来检测纵向数据随机效应模型中遗漏的混杂因素和非线性（Laird和Ware， 1982），这些模型具有随机斜率和个体间的截距。第一种方法比较回归系数内和回归系数之间的简单未加权，第二种方法是回归模型的Hausman规范检验，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x ^ i函数的显著性。最后一种方法的动机是当忽略群体或时期效应时产生的模型。我们比较了这三种方法，并说明了它们的应用。
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 ageing, 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 x̄i, 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.