Methods for analyzing nonstandard longitudinal datasets

分析非标准纵向数据集的方法

• 批准号: 327093-2009
• 负责人: Dubin, Joel
• 金额: $ 1.09万
• 依托单位: University of Waterloo
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=526818
• 关键词: Methods; analyzing; nonstandard; longitudinal; datasets

Longitudinal data analysis is an important and now well-studied subject in biostatistics with several excellent texts providing various viewpoints on the subject. However, there are three topics that we wish to emphasize in the current proposal and which are neither part of the focus of these texts nor of the biostatistical and statistical literature. The first is the modeling of longitudinal data when the response and any time-dependent covariate(s) are not measured at the same time points. A smoothing step is required, and we will focus upon a binning approach to handle this atypical data structure and allow for longitudinal modeling using traditional mixed effects models. The second topic is joint modeling of longitudinal and survival data when the longitudinal response is measured both retrospectively and prospectively, whereas the typical arrangement is that the longitudinal response is measured only prospectively, i.e., when the start of the clock of the survival process begins. A number of issues need to be addressed with this type of data, especially as regard to estimation bias when the amount of retrospective data differs between study participants. The third topic focuses on implementing a flexible model for longitudinal data that exhibit a gradual change from an increasing to decreasing trajectory over time (or vice versa). The challenge is to try to obtain a reasonably realistic model for the mean trajectory while not overfitting the data with a high number of difficult to interpret parameters, something that may occur with a nonparametric curve-based approach. We discuss the mixed-effects bent cable regression model to achieve this often satisfying compromise. This model will also allow us to account for between-subject heterogeneity in the trajectories including where the changes occur. HQP (graduate students) will be heavily involved in attempting to address all three of these topics in this proposal.

纵向数据分析是生物统计学中一个重要且现已得到充分研究的主题，有几篇优秀的文章提供了有关该主题的各种观点。 然而，我们希望在当前提案中强调三个主题，这些主题既不是这些文本的重点，也不是生物统计和统计文献的重点。 第一个是当响应和任何时间相关协变量不是在同一时间点测量时的纵向数据建模。 需要平滑步骤，我们将重点关注分箱方法来处理这种非典型数据结构，并允许使用传统混合效应模型进行纵向建模。 第二个主题是纵向和生存数据的联合建模，当纵向响应被回顾性和前瞻性地测量时，而典型的安排是纵向响应仅被前瞻性地测量，即当生存过程的时钟开始时。 此类数据需要解决许多问题，特别是当研究参与者之间的回顾性数据量不同时，估计偏差问题。 第三个主题侧重于实现纵向数据的灵活模型，该模型随着时间的推移呈现从增加到减少轨迹的逐渐变化（反之亦然）。 挑战在于尝试获得平均轨迹的相当现实的模型，同时又不会用大量难以解释的参数过度拟合数据，这种情况在基于非参数曲线的方法中可能会发生。 我们讨论混合效应弯曲电缆回归模型，以实现这种通常令人满意的折衷方案。 该模型还将使我们能够解释轨迹中受试者之间的异质性，包括发生变化的位置。 HQP（研究生）将积极参与尝试解决本提案中的所有这三个主题。