Nonparametric zero-inflated measurement error models and their applications

非参数零膨胀测量误差模型及其应用

• 批准号: RGPIN-2019-06043
• 负责人: CamirandLemyre, Felix
• 金额: $ 1.17万
• 依托单位: Université de Sherbrooke
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=759315
• 关键词: Nonparametric; zero; inflated; measurement; error

Dietary assessments are central to numerous investigations in various fields of life sciences, including nutrition, public health and epidemiology. In these assessments, researchers are often interested in capturing the long-term average intake, often called usual intake, of individual dietary components; and identifying consumption patterns and their connections to health outcomes. In this context, dietary intakes are often assessed using self-report instruments that allow to capture food and nutrient intake for a single day only. Because this snapshot cannot accurately reflect long-term average intake, it has long been recognized that such observations are versions of usual intakes contaminated by measurement errors.  When a food/nutrient is consumed daily and can be represented by a continuous variable, a vast literature on measurement errors shows how to seize the distribution of the individuals' usual intake or its relationship with health outcomes. However, there is arguably a greater interest in foods or nutrients that are consumed episodically such as alcohol, fish, milk, etc. Since these foods or nutrients are not consumed everyday, a complication that arises is that a non-negligible proportion of reported intake is equal to zero. This particularity is often referred to as one of excess zeros. For those variables, the reported intake is a mixture of a discrete and a continuous component, and none of the standard existing techniques developed in the measurement literature can be applied to successfully analyse such data.  Recently, some techniques specifically devoted to zero-inflated dietary data have been proposed in the literature, but these approaches are based on parametric assumptions, which can be hard to justify in practice. This proposal addresses this problem by targeting the design of new statistical approaches that relax as many of the strong parametric assumptions made in the existing literature as possible. As such, the primary concern of this research program is methodological, and aims to develop flexible nonparametric estimation procedures for a wide array of models involving observations such as those arising in dietary assessments, namely with zero-inflations and measurement errors. Focus will be directed towards settings that are encountered in real life by practitioners. Hence, the proposed methods will also allow to deepen our understanding of fundamental mechanisms in nutritional epidemiology and dietary patterns, by exposing features in datasets that are hidden otherwise under the constraints imposed by parametric models. Our understanding of fundamental patterns is at the heart of government policies and life-changing decisions. Consequently, the outcome of this proposal has to, and will, reach a broad audience that includes statisticians and practitioners engaged in diverse fields involving dietary assessments.

饮食评估是生命科学各个领域，包括营养学、公共卫生和流行病学的众多研究的核心。在这些评估中，研究人员通常有兴趣捕捉长期的平均摄入量，通常称为日常摄入量，个别饮食成分;并确定消费模式及其与健康结果的联系。在这种情况下，膳食摄入量往往是使用自我报告工具进行评估的，这种工具只能记录一天的食物和营养素摄入量。由于这一快照无法准确反映长期平均摄入量，因此长期以来，人们一直认为这些观察结果是受测量误差污染的日常摄入量版本。当一种食物/营养素每天消耗并且可以用连续变量表示时，大量关于测量误差的文献展示了如何抓住个人日常摄入量的分布或其与健康结果的关系。然而，可以说，人们对偶尔食用的食物或营养素更感兴趣，例如酒精，鱼，牛奶等，由于这些食物或营养素不是每天都食用，因此出现的并发症是报告的摄入量中不可忽略的比例等于零。这种特殊性通常被称为多余零之一。对于这些变量，报告的摄入量是一个离散和连续的组成部分的混合物，并没有标准的现有技术开发的测量文献中可以应用到成功地分析这样的data. Recently，一些技术专门致力于零膨胀的饮食数据已在文献中提出，但这些方法是基于参数的假设，这可能是很难证明在实践中。该提案通过设计新的统计方法来解决这一问题，这些方法尽可能放松现有文献中的强参数假设。因此，该研究计划的主要关注点是方法，旨在为涉及诸如饮食评估中出现的观察结果（即零通货膨胀和测量误差）的各种模型开发灵活的非参数估计程序。 重点将被引导到从业者在真实的生活中遇到的设置。因此，所提出的方法也将使我们加深对营养流行病学和饮食模式的基本机制的理解，通过暴露数据集中的特征，这些特征在参数模型施加的约束下被隐藏。 我们对基本模式的理解是政府政策和改变生活的决定的核心。因此，这一建议的结果必须而且将会达到广泛的受众，包括从事涉及饮食评估的不同领域的统计人员和从业人员。