Statistical methods for understanding multilevel and longitudinal data with application to health studies in South Wales

许多流行病学研究产生纵向数据，其中对相同的个人或群体进行重复测量。不幸的是，这些测量可能并不是在所有测量时间都适用于所有个人，这一问题通常被称为遗漏。最常见的失配类型是辍学，即个人未能通过一项测量，然后再也不会出现。如果人们因为变得越来越不健康而退学，那么随着时间的推移，幸存下来的人就会变得不那么有代表性，因为只有更健康的人留下来。尽管这可能会给从数据集中得出的结论带来偏差，但许多科学家仍然完全忽视了这个问题，因为现有的调整辍学的方法往往复杂而耗时。在这份奖学金中，我将发展我最近提出的处理辍学问题的技术，并将其应用于一项为期25年的瓦尔斯心血管疾病研究。此外，类似的方法也有可能应用于流行病学中另一种常见的结构，即人们聚集在社区中。虽然这些技术将适用于各种情况，但该研究金将特别侧重于来自南威尔士的两项健康研究，并将调查该地区身心健康的动态和地理。

Many epidemiological studies produce longitudinal data, where there are repeated measurements on the same individuals or groups. Unfortunately, these measurements may not be available for all individuals at all the measurement times, a problem known generically as missingness. The most common type of missingness is dropout, when individuals miss a measurement and then never reappear.If people drop out because they are becoming increasingly unhealthy, then over time the surviving individuals are become less representative, since only the healthier people remain. Though this can introduce bias into conclusions drawn from the dataset, many scientists still ignore the problem entirely because the methods available for adjusting for dropout tend to be complicated and time-consuming. In this fellowship I will develop my recently proposed technique for dealing with dropout, and apply it to a 25-year study of cardiovascular disease in Wales.Additionally, similar methods have the potential to be applied to another structure common in epidemiology, in which people cluster within neighbourhoods. While these techniques will be applicable in a wide variety of situations, this fellowship will focus in particular on two health studies from South Wales, and will investigate the dynamics and geography of physical and mental health in this region.