Latent Variable Growth Curve and Mixture Models for Life Course Epidemiology

生命过程流行病学的潜变量增长曲线和混合模型

• 批准号: G0902101/1
• 负责人: Darren Dahly
• 金额: $ 29.85万
• 依托单位: University of Leeds
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=G0902101%2F1
• 关键词: Latent; Variable; Growth; Curve; Mixture

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.

预防癌症和心血管疾病等慢性疾病需要更好地了解其病因。大多数人都熟悉吸烟和饮食等成年期危险因素的重要性。然而，研究表明，我们在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历程对疾病风险的影响，流行病学家对人群进行了长期跟踪（通常是从出生起）的研究。在过去的三十年里，此类研究变得越来越普遍，但尚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证明两种先进统计方法对生命历程研究人员的有用性。我将使用潜在生长曲线模型和生长混合模型，利用几项现有研究的数据来研究婴儿和儿童生长模式与肥胖的关系。研究结果的传播方式将有助于其他未经高级统计培训的研究人员使用这些方法。