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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=710237
• 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.

这项拟议的研究的目标是开发分析复杂临床和调查数据的创新方法。在临床研究中，我们经常观察到具有复杂相关性结构的纵向和事件间隔时间数据，这些数据具有许多重要问题，包括脆弱性、具有检测限(LOD)的测量、时间相关协变量以及后续测量中的异常值。纵向和生存数据的联合模型通常用于关注生存时间，并且希望调查内生的时间依赖的协变量对患者生存的影响。我将通过解决上述生存分析的复杂问题来开发和探索有效的方法。具体地说，我将研究在联合分析纵向数据和事件间隔时间数据时，可以限制离群值影响的稳健方法。 我还将研究临床研究中经常遇到的不完整纵向数据的有效和计算可行的方法。纵向数据的建模往往很复杂，因为响应变量并不总是在所有评估时间都能观察到。通常，遗漏过程被认为是不可忽略的，即遗漏取决于当时结果的未观察到的价值。要定义不可忽略的缺失响应随时间推移的完全可能性，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忽略的缺失响应的纵向数据，其中平均响应可以被描述为未知光滑函数的协变量的函数。 在调查抽样中，数据往往是成组相关的，重点是根据随机区域影响的预测因素估计区域平均值或比例(例如，在不同社会人口群体中认知受损的受试者的比例)。当感兴趣的结果变量是离散的或绝对的时，我将研究小区域估计的新方法。具体地说，我将在广义线性混合模型的框架内为聚集的相关数据开发一种稳健的方法。与普通的线性无偏估计不同，所提出的稳健估计不会受到数据中异常值的影响。 这些进展将使健康科学中的研究人员能够在存在遗漏观察和/或异常值的情况下，对其用于分析纵向和聚集相关数据的假设模型进行可靠的估计和评估，因此将极大地受益于处理复杂数据的临床从业者。