Statistical Methods for Irregularly Measured Longitudinal Data

不规则测量纵向数据的统计方法

• 批准号: RGPIN-2014-03989
• 负责人: Pullenayegum, Eleanor
• 金额: $ 0.8万
• 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: 2015
• 资助国家: 加拿大
• 起止时间: 2015-01-01 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=587536
• 关键词: Statistical; Methods; Irregularly; Measured; Longitudinal

We live in a data-rich society, and a particularly useful form of data is when the same questions or measures are collected repeatedly over time on the same subjects. This allows researchers to quantify trends over time. Often practical constraints lead to variation in the times at which measurements are taken. Indeed, sometimes study organisers may request that measurements be taken more often after an abnormal reading is observed. The end result is that the times at which measurements are taken is associated with the measurements themselves. In order to capture changes in outcomes over time correctly, analysis must account for the potential for the number and timing of measurements to be related to the outcomes; failure to do so could lead to a seriously distorted picture of the dynamics of the process over time. This work will focus on statistical methods that account for the fact that the timings of measurements, as well as the measurements taken at each visit, give important information about the outcomes of interest. Particular areas of focus will be improving the validity of methods, improving their efficiency, and improving their accessibility. All methods for this type of data make assumptions. The current approach to analysis risks violating some of these assumptions because it disregards important information on the visit process when setting up models. We propose an approach to analysis that makes better use of the available information and so reduces the risk of bias in the results. The most popular method of analysis for this type of data is known to be inefficient. That is, it does not extract as much information from the data as it could. This is wasteful because data are often expensive to obtain. There is a class of methods known as doubly robust methods that are generally more efficient, and we will aim to expand this class of methods to handle longitudinal data measured at irregular times. Finally, to be useful, methods need to be accessible. That is, they need to be simple enough that scientists doing routine analyses of data can use them. We propose to develop a new approach to analysis that is straightforward to use in practice, and likely also to be more efficient than the approaches that are currently used. The proposed work will thus strengthen the analysis of longitudinal data measured at irregular times by providing methods that improve validity, efficiency and accessibility.

我们生活在一个数据丰富的社会，一种特别有用的数据形式是，随着时间的推移，对同一主题重复收集相同的问题或措施。这使研究人员能够量化一段时间内的趋势。通常，实际约束导致进行测量的时间的变化。事实上，有时研究组织者可能会在观察到异常阅读后要求更频繁地进行测量。最终结果是，进行测量的时间与测量本身相关联。 为了正确地捕捉结果随时间的变化，分析必须考虑到测量的数量和时间与结果之间的可能联系;不这样做可能导致对过程随时间的动态的严重扭曲。这项工作将侧重于统计方法，这些方法考虑到测量的时间安排以及每次访问时进行的测量提供了有关感兴趣结果的重要信息。 特别关注的领域将是提高方法的有效性，提高其效率，并改善其可获得性。 这类数据的所有方法都有假设。目前的分析方法有违反其中一些假设的风险，因为它在建立模型时忽略了访问过程中的重要信息。我们提出了一种分析方法，可以更好地利用现有的信息，从而降低结果中的偏倚风险。 对这类数据最流行的分析方法是众所周知的效率低下。也就是说，它没有从数据中提取尽可能多的信息。这是一种浪费，因为获取数据通常很昂贵。有一类方法被称为双鲁棒方法，通常更有效，我们的目标是扩展这类方法来处理在不规则时间测量的纵向数据。 最后，要有用，方法必须是可访问的。也就是说，它们需要足够简单，以便科学家在进行常规数据分析时可以使用它们。我们建议开发一种新的分析方法，这种方法在实践中很容易使用，而且可能比目前使用的方法更有效。 因此，拟议的工作将通过提供提高有效性、效率和可获得性的方法，加强对不定期测量的纵向数据的分析。