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: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=566223
• 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.

我们生活在数据丰富的社会中，一种特别有用的数据是何时在同一主题上反复收集相同的问题或措施时。这使研究人员能够随着时间的推移量化趋势。通常，实际的约束会导致进行测量的时间变化。确实，有时研究组织者可能会要求在观察到异常阅读后更频繁地进行测量。最终结果是进行测量的时间与测量本身有关。为了正确地捕获结果随时间的变化，分析必须考虑到与结果相关的测量数量和时间的潜力；不这样做可能会导致随着时间的流逝而严重扭曲该过程的动态。这项工作将集中于统计方法，这些方法解释了以下事实：测量的时间以及每次访问时进行的测量值，提供有关感兴趣结果的重要信息。特定重点领域将提高方法的有效性，提高其效率并提高其可访问性。此类数据的所有方法都是假设。当前的分析方法可能会违反其中一些假设，因为它在设置模型时忽略了有关访问过程的重要信息。我们提出了一种分析方法，可以更好地利用可用信息，从而降低结果偏见的风险。已知最流行的此类数据分析方法效率低下。也就是说，它不会从数据中提取尽可能多的信息。这是浪费的，因为数据通常很昂贵。有一类称为双重鲁棒方法的方法通常更有效，我们将旨在扩展此类方法以处理在不规则时间测量的纵向数据。最后，为了有用，需要访问方法。也就是说，它们需要足够简单，以使科学家进行常规的数据分析可以使用它们。我们建议开发一种新的分析方法，该方法可以直接在实践中使用，并且可能比当前使用的方法更有效。因此，提出的工作将通过提供提高有效性，效率和可及性的方法来加强对在不规则时间测得的纵向数据的分析。