EFFECTS OF MISSPECIFICATION IN LONGITUDINAL MODELS

流行病学随访研究在过去5-10年中变得普遍。 本提案的目的是评价稳健性， 制定强有力的程序，并检测 回归模型的纵向数据分析所产生的， 问题研究 由于许多多元回归方法是新近才出现的， 使用不平衡的数据，这些方面的建模，这是重要的 实际应用还没有得到充分的探索。 模型误设定的两个常见例子是省略的协变量和 对关系的数学形式的错误假设。 这些 问题是密切相关的，因为省略的协变量可能导致 回归方程的形式。 相反，例如二次项 在回归方程中，可以被视为省略的协变量。 两种形式的 模型违反可能导致个体之间和个体内部的差异 回归系数，这是一个经常观察到的现象。 这种情况的一个例子是通过让 关注协变量x与遗漏混杂因素之间的相关性 z是不同的跨越比在个人。 这样的情况很常见 (e.g.流行病学研究中的群体和时期效应 老化）。 该模型结果的推广构成了 建议调查。 数据生成结构将被推广 在几个方向，对纵向数据分析的影响将是 考察 对现有的、 新颖的分析方法对具体模型的违规行为，将起到指导作用 选择和进一步发展稳健的方法。 由于影响 模型错误指定因分析方法而异，这也将是 研究作为一种潜在的方法，用于检测模型违规。 本提案中开发的方法将应用于统计 对几项正在进行的流行病学随访研究的数据进行分析。

Epidemiologic follow-up studies have become common in the last 5-10 years. The aims of this proposal pertain to the evaluation of robustness, development of robust procedures, and detection of misspecification in regression models for the analysis of longitudinal data arising from such studies. Because of the recency of many multivariate regression methods used with unbalanced data these aspects of modelling, which are important for practical application, have not yet been fully explored. Two common examples of model misspecification are omitted covariates and incorrect assumptions on the mathematical form of the relationship. These problems are closely related since omitted covariates can lead to change in the form of regression equations. Conversely, for example quadratic terms in regression equations can be viewed as omitted covariates. Both forms of model violations may lead to differences in across and within individual regression coefficients, an often observed phenomenon. One example of such a situation has been modelled by letting the correlation between the covariate of interest x, and an omitted confounder z be different across than within individuals. Such situations are common (e.g. cohort and period effects in epidemiologic studies of the effect of aging). Extension of the results for this model form the basis for the proposed investigation. The data generating structure will be generalized in several directions, and the impact on longitudinal data analysis will be examined. The investigation of the sensitivity of various existing and novel analysis methods to specific model violations, will serve as a guide to choice and further development of robust methods. Since the impact of model misspecification differs by analysis approach, this will also be investigated as a potential method for detecting model violation. The methods developed in this proposal will be applied in the statistical analysis of data from several ongoing epidemiologic follow-up studies.