Cutting Edge Survival Methods for Epidemiological Data

研究的主要主题是开发解决统计问题的新方法 我们团队在老龄化队列研究中的长期合作中出现的问题 人群和患有肾脏疾病的患者。我们专注于发展强大的 从延迟输入数据中估计回归参数的有效方法 流行的队列研究，在以下情况下做出适当和有效的统计推断 协变量受到删减和测量误差的影响，并正在开发新的 对终端事件对纵向测量的影响进行最佳建模的策略。 我们还计划开发公开可用的统计软件，目标是 传播和推广。 所提出的延迟录入方法是基于扩展的条件 在不指定截断分布的情况下从截断时间构造的似然， 这导致了一个更有效的估计器。回归模型的改进方法 对于带有删失协变量的纵向数据，解决了 常用的非参数方法和参数方法。例如，非参数 方法无法恢复删除协变量的任何尾部信息，原因是 检测，而参数方法可能会由于模型的原因而产生偏差的结果 说明有误。我们的方法将促进协变量的研究，受以下限制 检测与测量误差一起，反映了许多实验室措施的真实性。 航站楼发生时纵向数据的统计模型 事件反映了响应变量和协变量之间的关系 与通常的边际模型大致相同(不考虑终端 事件)当数据在远离终端事件处被观察到，但变得严重时 取决于数据采集时间接近终端时的终端事件时间 事件。这些方法的动机来自于并将广泛应用于 数据集，包括肾脏疾病进展数据、肾移植登记 数据，女性健康纵向数据收集自密歇根州骨健康和 代谢研究和全国妇女健康研究，以及 纵向终末期肾病医疗费用数据。这些方法将得到广泛的应用 适用于生物医学研究的许多其他领域的问题。

The main theme of the research is to develop new methodologies for resolving statistical issues emerging from our team’s long-term collaborations in cohort studies of aging populations and patients with kidney disease. We focus on developing robust and efficient estimating procedures for regression parameters from data with delayed entry in prevalent cohort studies, making appropriate and efficient statistical inference when covariates are subject to censoring and measurement error, and developing new strategies that best model the effects of terminal events on longitudinal measurements. We also plan to develop publicly available statistical software with the goal of dissemination and generalization. The proposed approach for delayed entry is based on an augmented conditional likelihood constructed from truncation times without specifying the truncation distribution, which leads to a more efficient estimator. The proposed approach for regression models for longitudinal data with censored covariates addresses some serious issues with the common nonparametric and parametric methods. For example, the nonparametric methods cannot recover any tail information for the censored covariates due to limit of detection, while the parametric methods may yield biased results due to model misspecification. Our method will facilitate investigation of covariates subject to limit of detection together with measurement error, reflecting the reality of many lab measures. The proposed statistical models for longitudinal data with the occurrence of a terminal event reflect the reality that the relationship between a response variable and covariates is approximately the same as the usual marginal model (without considering terminal event) when data are observed far from the terminal event, but becomes heavily dependent on the terminal event time when data collecting time is close to the terminal event. These methods are motivated from and will be applied to a wide range of datasets, including the kidney disease progression data, the kidney transplant registry data, the women’s health longitudinal data collected from the Michigan Bone Health and Metabolism Study and the Study of Women's Health Across the Nation, and the longitudinal end stage renal disease medical cost data. The methods will be widely applicable to problems in many other fields of biomedical research.