Longitudinal data subject to irregular observation: developing methods for variable selection, causal inference, and measurement error

不规则观察的纵向数据：开发变量选择、因果推断和测量误差的方法

• 批准号: RGPIN-2021-02733
• 负责人: Pullenayegum, Eleanor
• 金额: $ 1.75万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=764379
• 关键词: Longitudinal; data; subject; irregular; observation

We live in a data-rich society, and many datasets include information on the same individuals repeatedly over time (known as longitudinal data). However, often the times at which data is collected are related to the outcome of interest. For example, in a study of newborn growth, newborns who grow slowly are likely to have their weight measured more often. Taking the average of all the weight measurements over time will underestimate the growth rate. The measurement times are informative: the frequency of measurements gives us important information about the value of the measurements. We must take the measurement frequency into account to get an accurate picture of the trajectory over time. Methods for analysing this type of data are limited in what they can do. This work will focus on: (a) handling datasets for which a large number things are measured at each time point; (b) enabling researchers to detect whether one variable causes a change in another; (c) correctly accounting for measurement error; (d) describing the behaviour of a system according to whether it is in a high or low risk state. (a)Many datasets contain a large amount of information. For example, data on water quality might include the levels of thousands of contaminants; we need to filter out which contaminants are important. We will develop ways of doing this for longitudinal data with informative measurement times. (b)It is often straightforward to show whether one quantity tends to increase as another increases (association), but harder to show whether a change in one quantity causes a change in another (causality). Our ability to determine causality with longitudinal data with informative measurement times is limited. We will develop approaches to address this gap. (c)Data is typically measured subject to error. For example, height is never measured perfectly. This can lead to bias unless it is accounted for. We will find ways of doing this with longitudinal data with informative measurement times. (d)It is often helpful to describe expected outcomes conditional on risk status. For example, for a patient with a chronic disease subject to relapse and remission, it may be more helpful to describe health during periods of relapse, health during periods of remission, and the proportion of time spent in remission. We will develop ways of doing this using longitudinal data with informative observation times. In Canada we collect a large amount of data as part of usual societal operations. For example, many patients provide consent for their medical records to be used for research. This data can address questions that are specific to Canada, for example, to determine which groups of people are at risk of poorer health outcomes. This requires careful handling of informative observation in order to provide reliable results. The methods we develop will equip researchers to do this, and so generate high quality evidence on which to base societal decisions.

我们生活在数据丰富的社会中，许多数据集随着时间的推移反复（称为纵向数据），包括有关同一个人的信息。但是，收集数据的时间通常与感兴趣的结果有关。例如，在对新生儿生长的研究中，慢慢生长的新生儿可能会更频繁地测量自己的体重。随着时间的推移，将所有重量测量的平均值取得长足，将低估增长率。测量时间是有益的：测量频率为我们提供了有关测量值的重要信息。我们必须考虑测量频率，以准确地了解轨迹的时间。分析此类数据的方法在他们可以做的事情上受到限制。这项工作将重点关注：（a）处理在每个时间点都测量大量内容的数据集； （b）使研究人员能够检测一个变量是否导致另一个变量的变化； （c）正确考虑测量误差； （d）根据系统的行为是高风险状态还是低风险状态。 （a）许多数据集包含大量信息。例如，关于水质的数据可能包括数千种污染物的水平；我们需要过滤出哪些污染物很重要。我们将开发出有信息测量时间的纵向数据的方法。 （b）通常很容易表明一个数量是否会随着另一个数量的增加（关联）而增加，但是很难表明一种数量的变化是否会导致另一种变化（因果关系）。我们使用纵向数据确定因果关系的能力，并有限的测量时间有限。我们将开发解决这一差距的方法。 （c）通常对数据进行测量。例如，从永远无法完美地测量高度。除非考虑到，否则这可能会导致偏见。我们将找到使用纵向数据的方法，并具有信息的测量时间。 （d）描述有条件的风险状况，通常会有所帮助。例如，对于患有复发和缓解的慢性疾病的患者，描述在复发期，缓解期间的健康以及缓解时间所花费的时间比例可能会更有帮助。我们将使用纵向数据和内容丰富的观察时间来开发这样做的方法。在加拿大，我们收集了大量数据，作为通常的社会运营的一部分。例如，许多患者同意其病历用于研究。这些数据可以解决特定于加拿大的问题，例如，确定哪些人群有较差的健康状况。这需要仔细处理信息性观察，以提供可靠的结果。我们开发的方法将使研究人员能够做到这一点，从而为基于社会决策做出基础的高质量证据。