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Development and application of statistical methods for addressing the heterogeneity of data collection intervals common in longitudinal datasets

开发和应用统计方法来解决纵向数据集中常见的数据收集间隔的异质性

基本信息

批准号：
1943044
负责人：
金额：
--
依托单位：
University of Leeds
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2017
资助国家：
英国
起止时间：
2017 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-1943044
关键词：
Development application statistical methods addressing

项目摘要

There is great interest in many sciences in gathering data over time and analysing patterns of change. Examples include the study of growth curves, psychological changes, and changes in the prevalence of diseases in an area. Often, researchers examine the relationship of these patterns of data (longitudinal exposures) to later events (outcomes), which requires the use of data analysis techniques that describe patterns of the longitudinal exposure in individuals (e.g. growth curves in individual people). There are a number of techniques that can be used to do this. Multilevel models (MLMs) start by describing the average pattern of the longitudinal exposure, but also give information on how much individual patterns differ from it; the results from these models are easy to understand but they cannot describe very complicated patterns. On the other hand, latent growth curve models (LGCMs) estimate individual patterns of a longitudinal exposure by creating extra data that describes them, rather than an average trajectory. To do this, latent growth curve models represent time in an unusual way - by adding a 'factor loading' relating the data describing the curves to each measurement of the longitudinal exposure. This factor loading is often set to the time at which the measurement was taken, but can be estimated by the model, which allows for very complex curves to be represented by LGCMs much more easily than in MLMs. LGCMs can also be extended to growth mixture models (GMMs), which identify underlying subgroups in the data based on the types of patterns of the longitudinal exposure. However, LGCMs require the data in all individuals to be measured at exactly the same time points - called 'interval homogeneity'. However, this is rarely the case in practice, especially when using observational data (e.g. children's growth curves recorded as measurements in their medical records); thus, these most flexible modelling techniques cannot be used widely. Another method that can be used to describe longitudinal exposures is functional data analysis (FDA). This describes individual patterns of the longitudinal exposure by fitting smooth curves in smaller time segments. These segments are bounded by 'knots', the number and position of which are chosen by the researcher. This is also a flexible method, but can be inaccurate when there exist wide spaces between measurements of the longitudinal exposure. This project aims to examine the utility of carrying out FDA on a longitudinal exposure without interval homogeneity by using the individual patterns this describes to interpolate individual measurements and create interval homogeneity, thereby allowing for the use of latent growth curve modelling to analyse the patterns of the longitudinal exposure while relating this to a later outcome. These aims will be addressed using real and simulated data, and the following questions will also be addressed: a) How can the optimum points for interpolation of measurements be found?; and b) How should the optimum 'basis function' be chosen (i.e. the types of curves used to fit segments of the longitudinal exposure in FDA)?. The results will also be compared to those from LGCMs (which assume interval homogeneity) and from MLMs.

随着时间的推移，收集数据和分析变化模式是许多科学的极大兴趣。例如，研究一个地区的生长曲线、心理变化和疾病患病率的变化。通常，研究人员检查这些数据模式(纵向暴露)与后来的事件(结果)的关系，这需要使用数据分析技术来描述个人的纵向暴露模式(例如，个人的生长曲线)。有许多技术可以用来实现这一点。多水平模型(MLM)从描述纵向曝光的平均模式开始，但也提供了关于单个模式与其有多大差异的信息；这些模型的结果很容易理解，但它们不能描述非常复杂的模式。另一方面，潜在增长曲线模型(LGCM)通过创建描述纵向暴露的额外数据来估计纵向暴露的个别模式，而不是平均轨迹。为了做到这一点，潜在增长曲线模型以一种不寻常的方式表示时间-通过添加一个将描述曲线的数据与纵向曝光的每一次测量相关联的“因子加载”。这一因素负荷通常被设置为进行测量的时间，但可以通过模型进行估计，这使得LGCM比MLM更容易表示非常复杂的曲线。LGCM还可以扩展到生长混合模型(GMM)，该模型根据纵向暴露的模式类型确定数据中的潜在亚组。然而，LGCM要求所有个体的数据在完全相同的时间点上进行测量--称为“区间同质性”。然而，在实践中很少出现这种情况，特别是在使用观测数据时(例如，儿童的生长曲线在他们的医疗记录中被记录为测量值)；因此，这些最灵活的建模技术不能广泛使用。另一种可用于描述纵向暴露的方法是功能数据分析(FDA)。这通过在较小的时间段中拟合平滑曲线来描述纵向曝光的个别图案。这些线段以“结”为界，其数量和位置由研究人员选择。这也是一种灵活的方法，但当纵向曝光的测量之间存在较大间隔时，可能会不准确。本项目旨在通过使用本文所描述的个体模式来插入个体测量并创建区间同质性，从而检验对没有间隔均质的纵向暴露进行FDA的效用，从而允许使用潜在生长曲线建模来分析纵向暴露的模式，同时将其与以后的结果相关联。这些目标将使用真实数据和模拟数据来实现，还将解决以下问题：a)如何找到测量值的最佳插值点？；b)如何选择最佳的‘基函数’(即FDA用来拟合纵向曝光分段的曲线类型)？结果还将与LGCM(假设区间均匀)和MLMS的结果进行比较。