Latent Variable Growth Curve and Mixture Models for Life Course Epidemiology

预防癌症和心血管疾病等慢性疾病需要更好地了解其原因。成年风险因素的重要性，如吸烟和饮食，是大多数人所熟悉的。然而，研究表明，我们成年后面临的许多疾病也可能受到我们在童年，婴儿期甚至出生前经历的因素的影响。为了更好地了解这些生命过程对疾病风险的影响，流行病学家研究了长期随访的人群，通常从出生开始。在过去的30年里，这类研究变得越来越普遍，但目前还不完全清楚哪种统计方法最适合分析这些数据。我的研究旨在探索和展示生命历程研究人员的两个先进的统计方法的有用性。使用潜在生长曲线模型和生长混合模型，我将研究如何婴儿和儿童的生长模式与肥胖，使用现有的几项研究的数据。研究结果的传播方式将便利没有受过高级统计培训的其他研究人员使用这些方法。

Preventing chronic illnesses such as cancer and cardiovascular disease requires a better understanding of their causes. The importance of adulthood risk factors such as smoking and diet are familiar to most people. However, research suggests that many of the diseases we face in adulthood may also be influenced by factors we experience in childhood, infancy, and even before we are born. To better understand these life course influences on disease risk, epidemiologists study cohorts of people that are followed up for a long period of time, often from birth. These kinds of studies have become more common over the past thirty years, but it is not entirely clear which statistical methods are best suited to analyze these data. My research aims to explore and demonstrate the usefulness of two advanced statistical methods for life course researchers. Using latent growth curve models and growth mixture models, I will investigate how infant and childhood growth patterns are related to obesity, using data from several existing studies. Research findings will be disseminated in a manner that facilitates the use of these methods by other researchers without advanced statistical training.