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: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=635836
• 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.

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，我将研究鲁棒的方法，可以约束的影响，离群值时，联合分析纵向和时间到事件的数据。