STATISTICAL METHODS IN LONGITUDINAL STUDIES OF DEMENTIA

描述：(改编自《调查者摘要》)纵向 阿尔茨海默病相关痴呆的流行病学研究使用了 估计总体特征的两阶段抽样设计，例如 疾病发病率和疾病与推定风险之间的关系 各种因素。对来自这些研究的数据的统计分析因 纵向设计，复杂的抽样方案，各种磨损模式，以及 令人感兴趣的多重结果。特别是，目前的统计方法有 都未能解决因老年人死亡而遗失结果的问题 对象，这很可能是基于可能性的推理不可忽视的。它 众所周知，忽略丢失数据问题的分析可能会导致 结果有偏见。建议的研究项目是开发统计方法 对于存在不可忽略的纵向痴呆数据的分析 因死亡而丢失数据。具体地说，提出了三种方法。第一 是一种选择模型方法，其中由于死亡机制而丢失的数据是 根据死亡前的疾病状况进行建模。第二种是混合物 对疾病结局和死亡过程进行建模以共享的模型方法 一组常见的随机效果参数，被认为代表受试者的 疾病和死亡的力量(或易感性)。三是做好模范 疾病发病率和死亡率同时使用疾病-死亡 随机模型。申请者还提出了一些设计问题。 纵向痴呆症研究并考虑对错误分类进行调整 估计疾病与风险之间的关联性时的风险暴露错误 各种因素。申请者希望拟议的研究能够导致统一和 在纵向痴呆症研究中的有效统计方法，从而减少 由于统计学原因导致的研究结果的偏差、低效和变化 分析方法。申请人指出，在本申请中开发的方法 建议广泛适用于其他流行病学研究，使用 纵向两阶段抽样设计。

DESCRIPTION: (Adapted from the Investigator's Abstract) Longitudinal epidemiological studies in Alzheimer's disease related dementia have used two-phase sampling designs to estimate population characteristics such as disease incidence and the associations between disease and putative risk factors. Statistical analyses on data from these studies are complicated by the longitudinal design, complex sampling schemes, various attrition patterns, and multiple outcomes of interest. In particular, current statistical methods have all failed to deal with the problem of missing outcomes due to death of elderly subjects, which is likely to be non-ignorable to likelihood based inference. It is well known that analyses ignoring the missing data problem may lead to biased results. The proposed research project is to develop statistical methods for the analysis of longitudinal dementia data in the presence of non-ignorable missing data due to death. Specifically, three methods are proposed. The first is a selection model approach where the missing data due to death mechanism is modeled to depend on disease status prior to death. The second is a mixture model approach where the disease outcome and death process are modeled to share a common set of random effect parameters thought to represent the subjects' strength (or susceptibility) for both disease and death. The third is to model disease incidence and mortality simultaneously using an illness-death stochastic model. The applicants also address some questions for designing longitudinal dementia studies and consider adjustment for misclassification error on risk exposure when estimating association between disease and risk factors. The applicants hope that the proposed research can lead to unified and efficient statistical methods in longitudinal dementia studies and hence reduce the bias, inefficiency, and variation in study results due to statistical analysis approach. The applicant states that the methods developed in this proposal are widely applicable to other epidemiological studies using longitudinal two-phase sampling designs.