EFFECTS OF MISSPECIFICATION IN LONGITUDINAL MODELS

描述（申请人的摘要）：该提案的目的是 统计分析方法的开发、评估和应用 纵向数据，重点是观察性后续研究 流行病学和其他健康相关研究。具体重点是 真实地模拟与常用假设的各种偏差 模型，评估效果，考虑稳健的新估计技术 可能的错误规格并开发检测方法 规格错误。要考虑的错误规格被忽略了混杂因素， 测量误差和信息缺失数据。将会受到特别关注 理清它们之间的关系，并引入一个统一的框架 只要有可能。 拟议的工作包括： - 拟合具有潜在随机效应的模型，以捕获可能的随机效应 规格错误 - 开发简单的方法来拟合信息缺失数据的模型 - 针对测量误差问题采用半参数随机效应模型 - 对混杂因素的影响进行全面调查 广义线性模型，以及 - 开发伪分数测试以检测 GEE 的错误指定 该方法将应用于大量纵向分析 有关睡眠障碍、糖尿病、老年人跌倒、新生儿肺脏的数据 人口老龄化中的疾病和眼部疾病。

DESCRIPTION (Applicant's abstract): The aims of this proposal are the development, evaluation and application of methods for the statistical analysis of longitudinal data, with emphasis on observational follow-up studies in epidemiologic and other health related research. The specific focus is to realistically model various departures from the assumptions of commonly applied models, evaluate the effect, consider new estimation techniques that are robust to the possible misspecification and develop methods for detecting the misspecification. Misspecifications to be considered are omitted confounders, measurement error and informative missing data. Special attention will be paid to the relationships between these, and a unified framework will be introduced whenever possible. The proposed work includes: - Fitting models with latent random effects geared to capture possible misspecifications - Developing simple methods for fitting models for informative missing data - Adapting semiparametric random effects models for measurement error problems - Undertaking a comprehensive investigation of the effect of confounding in generalized linear models, and - Developing of a pseudo score test to detect misspecification with GEE The methods will be applied in the analysis of large amounts of longitudinal data on sleep disorders, diabetes, falls among the elderly, neonatal lung disease and eye disease in an aging population.