Modelling within-individual variation in repeated continuous exposures

对重复连续暴露中的个体差异进行建模

• 批准号: MR/N027485/1
• 负责人: Kate Tilling
• 金额: $ 38.47万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 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=MR%2FN027485%2F1
• 关键词: Modelling; within; individual; variation; repeated

Measurements of biological and emotional states are often made repeatedly for the same person. These are used to monitor people with different health problems, or to alert people to health risks. Examples include the measurement of prostate specific antigen (PSA) every 3 months in monitoring of low-risk prostate cancer, and measurement of blood pressure at antenatal appointments to identify risk of pre-eclampsia in pregnant women. Measures are collected at varying intervals, but typically in months (PSA) or weeks (blood pressure during pregnancy) rather than days. Intensively-collected measures are made increasingly possible by new technology, including 'apps' on smartphones such as those which ask about mood at intervals throughout the day. Other examples include blood glucose monitoring in pregnant mothers for detecting and reducing the risk of delivery of a high-birthweight baby, and 24-hour blood pressure monitoring in routine clinical practice.With these repeated measurements, doctors are often interested in the level of the measure (e.g. does a pregnant woman have high blood pressure), or in how fast the measure is rising (e.g. a rising PSA might indicate a worsening of prostate cancer). However, there is growing interest in whether the variability, or fluctuation, of an individual's measures might also be related to health. For example, how much a person's mood goes up and down, and the timing of these fluctuations, may tell us more about their mental health than just whether they tend to have high or low mood on average. Similarly, someone with blood pressure which varies a lot may be at higher risk of future heart attack or stroke than someone with more stable blood pressure. There are many separate features of the way a measure varies for an individual which may predict future disease. One could be called the 'amplitude', and is a measure of how many very high and very low values a person experiences (for example, the difference between the very highest and very lowest blood pressures a person experiences during one day). Another is 'variation', and is a measure of how much the person's measure changes for their overall average. Disentangling these features of variation may be important in some situations. For example, it could be that it is the variation in blood pressure which predicts future heart attacks, but the amplitude of blood pressure during pregnancy which predicts problems for mother and child. There have been many statistical methods developed to relate the level or change in a person's measures to a future disease, but far fewer to look at variation in a person's measurements. In this project, we aim to develop these much-needed statistical methods for modelling fluctuation in measures within an individual, and relating that fluctuation to risk factors and later disease. The developments we will make in this project will allow clinicians to make the best possible use of measures taken repeatedly within people in predicting their future health. We will make sure our new methods are freely available to all scientists in a user-friendly format.We will apply these methods to two studies that have previously been published. The first is blood pressure readings during pregnancy from mothers whose children were part of ALSPAC, and relating these to the health of the child at birth. The second is a study that monitored blood pressure at 15-minute intervals over one day, and relating variation in this to later cardiovascular health.

对同一个人的生物和情绪状态的测量经常被重复进行。这些用于监测患有不同健康问题的人，或提醒人们注意健康风险。例如，每3个月测量一次前列腺特异性抗原（PSA），以监测低风险前列腺癌，并在产前预约时测量血压，以确定孕妇患先兆子痫的风险。测量数据的收集间隔不同，但通常以月（PSA）或周（怀孕期间的血压）为单位，而不是以天为单位。新技术使密集收集的测量变得越来越可能，包括智能手机上的“应用程序”，例如那些在一天中每隔一段时间询问情绪的应用程序。其他例子包括孕妇的血糖监测，以检测和降低高出生体重婴儿的风险，以及常规临床实践中的24小时血压监测。通过这些重复测量，医生通常对测量水平感兴趣（例如孕妇是否有高血压），或测量上升的速度（例如PSA上升可能表明前列腺癌恶化）。然而，人们越来越感兴趣的是，一个人的措施的可变性或波动是否也可能与健康有关。例如，一个人的情绪起伏多少，以及这些波动的时间，可能会告诉我们更多关于他们的心理健康，而不仅仅是他们平均情绪是高还是低。同样，血压变化很大的人未来心脏病发作或中风的风险可能高于血压稳定的人。有许多不同的特征可以预测未来的疾病。一个可以被称为“振幅”，是衡量一个人经历了多少非常高和非常低的值（例如，一个人在一天内经历的最高和最低血压之间的差异）。另一个是“变异”，是衡量一个人的测量值相对于他们的总体平均值变化的程度。在某些情况下，解开这些变异特征可能很重要。例如，可能是血压的变化预测未来的心脏病发作，但怀孕期间血压的幅度预测母亲和孩子的问题。已经开发了许多统计方法来将一个人的测量值的水平或变化与未来的疾病联系起来，但很少有人来研究一个人的测量值的变化。在这个项目中，我们的目标是开发这些急需的统计方法，用于模拟个体内测量值的波动，并将这种波动与风险因素和后期疾病联系起来。我们将在这个项目中取得的进展将使临床医生能够最好地利用人们在预测他们未来健康时反复采取的措施。我们将确保我们的新方法以用户友好的格式免费提供给所有科学家。我们将把这些方法应用于以前发表的两项研究。第一个是母亲怀孕期间的血压读数，其子女是ALSPAC的一部分，并将这些读数与出生时的儿童健康联系起来。第二项研究是在一天内以15分钟的间隔监测血压，并将其变化与后来的心血管健康联系起来。

项目成果

Joint modelling of individual trajectories, within-individual variability and a later outcome: systolic blood pressure through childhood and left ventricular mass in early adulthood

个体轨迹、个体内变异性和后期结果的联合建模：儿童时期的收缩压和成年早期的左心室质量

• DOI: 10.1101/19008474
• 发表时间: 2019
• 作者: Parker R

Modeling Risk Factors for Intraindividual Variability: A Mixed-Effects Beta-Binomial Model Applied to Cognitive Function in Older People in the English Longitudinal Study of Ageing.

• DOI: 10.1093/aje/kwad169
• 发表时间: 2024-01-08
• 期刊: American journal of epidemiology
• 影响因子: 5

The dynamic interplay between sleep and mood: an intensive longitudinal study of individuals with bipolar disorder.

• DOI: 10.1017/s0033291721005377
• 发表时间: 2023-06
• 期刊: PSYCHOLOGICAL MEDICINE
• 影响因子: 6.9
• 作者: Lewis, K. J. S.;Tilling, K.;Gordon-Smith, K.;Saunders, K. E. A.;Di Florio, A.;Jones, L.;Jones, I;O'Donovan, M. C.;Heron, J.
• 通讯作者: Heron, J.

Mixed-effects location scale models for joint modelling school value-added effects on the mean and variance of student achievement

用于联合建模学校增值对学生成绩均值和方差的混合效应位置尺度模型

• DOI: 10.48550/arxiv.2110.02079
• 发表时间: 2021
• 作者: Leckie G