Statistical methods for complex clinical and survey data

复杂临床和调查数据的统计方法

• 批准号: RGPIN-2016-06258
• 负责人: Sinha, Sanjoy
• 金额: $ 1.6万
• 依托单位: Carleton University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665304
• 关键词: Statistical; methods; complex; clinical; survey

The proposed research has the goal of developing innovative methods for analyzing complex clinical and survey data. In clinical studies, we often observe longitudinal and time-to-event data with complex dependence structures featuring many important issues including frailties, measurements with the limit of detection (LOD), time-dependent covariates, and outliers in follow-up measurements. Joint models for longitudinal and survival data are typically used when the focus is on the survival times and one wishes to investigate the effect of an endogenous time-dependent covariate on the survival of patients. I will develop and explore efficient methods by addressing the aforementioned complex issues of survival analysis. Specifically, I will investigate robust methods that can bound the influence of outliers when jointly analyzing the longitudinal and time-to-event data.****I will also investigate efficient and computationally feasible methods for incomplete longitudinal data commonly encountered in clinical studies. The modeling of longitudinal data is often complicated by the fact that the response variable is not always observed at all assessment times. Often the missingness process is considered nonignorable, i.e., the missingness depends on the unobserved value of the outcome at that time. To define a full likelihood for nonignorable missing responses over time, one needs to specify a joint distribution for the repeated outcomes as well as a model for the missing data mechanism. I will develop and study a semi-parametric approach to analyzing longitudinal data with non-ignorable missing responses, where the mean response may be described as a function of the covariates by an unknown smooth function.****In survey sampling, data are often clustered correlated and the focus is on the estimation of area means or proportions (e.g., proportions of subjects who are cognitively impaired in different socio-demographic groups) based on predictors of random area effects. I will investigate novel methods for small area estimation when the outcome variable of interest is discrete or categorical. Specifically, I will develop a robust method in the framework of generalized linear mixed models for clustered correlated data. Unlike ordinary linear unbiased estimators, the proposed robust estimators will not be influenced by outliers in the data.****The developments will enable researchers in the health sciences to engage in reliable estimation and assessment of their hypothesized models for analyzing longitudinal as well as clustered correlated data in the presence of missing observations and/or outliers, and hence will greatly benefit clinical practitioners working with complex data.

这项研究的目标是开发创新方法来分析复杂的临床和调查数据。在临床研究中，我们经常观察到具有复杂依赖结构的纵向和时间事件数据，这些数据具有许多重要问题，包括脆弱性，检测限（LOD）测量，时间依赖性协变量和随访测量中的离群值。纵向和生存数据的联合模型通常用于关注生存时间，并且希望研究内源性时间依赖性协变量对患者生存的影响。我将通过解决上述复杂的生存分析问题来开发和探索有效的方法。具体来说，我将研究在联合分析纵向和事件发生时间数据时可以限制离群值影响的稳健方法。*我还将研究有效的和计算上可行的方法，不完整的纵向数据在临床研究中经常遇到的。纵向数据的建模往往是复杂的事实，即响应变量并不总是在所有评估时间观察。缺失过程通常被认为是不可解释的，即，缺失程度取决于当时未观察到的结果值。为了定义一段时间内不可重复的缺失响应的完全可能性，需要指定重复结果的联合分布以及缺失数据机制的模型。我将开发和研究一种半参数方法来分析具有不可重复缺失响应的纵向数据，其中平均响应可以通过未知的平滑函数描述为协变量的函数。在调查抽样中，数据通常是聚类相关的，重点是估计面积均值或比例（例如，不同社会人口学群体中认知受损受试者的比例）。我将研究新的方法，小面积估计时，结果变量的利益是离散或分类。具体来说，我将开发一个强大的方法在框架内的广义线性混合模型的聚类相关数据。与普通线性无偏估计不同，本文提出的稳健估计不会受到数据中离群值的影响。*这些发展将使健康科学领域的研究人员能够对他们的假设模型进行可靠的估计和评估，以便在存在缺失观测值和/或离群值的情况下分析纵向和聚类相关数据，因此将极大地有利于处理复杂数据的临床从业人员。