Constrained Statistical Inference

约束统计推断

• 批准号: 10925000
• 负责人: SHYAMAL PEDDADA
• 金额: $ 100.73万
• 依托单位: NATIONAL INSTITUTE OF ENVIRONMENTAL HEALTH SCIENCES
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10925000
• 关键词: Biological; Biology; Biomedical Research; Birth; Birth Weight; Book Chapters; Child; Communication; Complex; Data; Data Analyses; Data Collection; Data Set; Disease; Disparate; Dose; Ecology; Ecosystem; Endocrinology; Experimental Designs; Exposure to; Fetal Development; Genes; Health; Hormones; Hour; Human; Human Chorionic Gonadotropin; Human Microbiome; Immune response; Infant; Inflammation; Linear Regressions; Literature; Manuscripts; Measures; Mediation; Mediator; Methodology; Methods; Microbe; Modeling; Nature; Operative Surgical Procedures; Outcome; Patients; Pattern; Periodicity; Persons; Pesticides; Physical activity; Population; Procedures; Publications; Publishing; Reporting; Research; Research Personnel; Shapes; Soil; Sum; Time; Work; circadian pacemaker; feeding; fetal; gut microbiome; gut microbiota; infant gut microbiome; interest; microbiome; microbiome research; novel; pesticide exposure; prenatal testing; response; screening program; sleep pattern; stool sample; vector

We have been working on several research problems in this project during this reporting year. Here are some accomplishments. There is growing evidence in the literature demonstrating that the (gut) microbiome is involved in inflammation and immune response, and hence human health and disease. Thus, there is considerable interest among biomedical researchers to study the human microbiome. Since microbes form an ecology, and hence are potentially inter-dependent, there is considerable interest to describe associations among them. Standard methods such as the Pearson correlation is not valid because the observed data are compositional, i.e., one gets to measure only the relative abundances of various microbes in a given ecosystem such as the stool sample. In this project a formal statistical methodology was developed to estimate correlations. The resulting methodology was illustrated using an infant gut microbiome data. An infants gut ecology continuously evolves during the first year after birth due to various factors such as changes in feeding, sleep patterns, exposure to people and so on. Using this novel methodology, for the first time in the literature, we describe associations among infant gut microbiota at different time points during the first year after birth. This manuscript was published in Nature Communications (Lin, Eggesbo and Peddada, Nature Communications, 2022). Microbiome differential abundance analysis methods for a pair of groups are well established in the literature. However, many microbiome studies involve multiple groups, sometimes even ordered groups, such as stages of a disease, and require different types of comparisons. Standard pairwise comparisons are not only inefficient in terms of power and false discovery rates, but they may not address the scientific question of interest. In this project, a general framework was developed for performing a wide range of multi-group analyses with covariate adjustments and repeated measures. The resulting methodology is illustrated using two real data sets. The first example explores the effects of aridity on the soil microbiome, and the second example investigates the effects of surgical interventions on the microbiome of IBD patients. The manuscript is under review. In many applications researchers are interested in multivariate outcomes that are compositional, i.e., the observed data sum to a constant. For example, the activities of a child in 24-hour period. However, for various reasons, including the method of collection of data, sometimes not all variables are measured and hence we have missing values in the multivariate compositional vector. Assuming that the missingness is associated with covariates, a simple multiple imputation methodology called Multiple Imputation for Compositional Data (MICoDa) is developed in this project to impute the missing values in a compositional vector. MICoDa is illustrated using two very disparate types of data where the missing values arise for different reasons. The first example relates to 24-hour physical activity data of young children and the second example relates to a gut microbiome data. This manuscript is in press for publication as an invited book chapter. Often linear regression is used to perform mediation analysis. However, in many instances, the underlying relationships may not be linear, as in the case of placental-fetal hormones and fetal development. Furthermore, the exact functional form of the relationship is generally unknown. For these reasons, we develop a novel shape-restricted inference-based methodology for conducting mediation analysis. This work is motivated by an application in fetal endocrinology where researchers are interested in understanding the effects of pesticide application on birth weight, with human chorionic gonadotropin (hCG) as the mediator. We assume a practically plausible set of nonlinear effects of the hCG on the birth weight and a linear relationship between the pesticide exposure and the hCG, with both exposure-outcome and exposure-mediator models being linear in the confounding factors. Using the proposed methodology on a population-level prenatal screening program data, with hCG as the mediator, we discovered that, while the natural direct effects suggest a positive association between pesticide application and birth weight, the natural indirect effects were negative.

在本报告年度，我们一直在研究该项目中的几个研究问题。 以下是一些成就。 文献中有越来越多的证据表明，（肠道）微生物组参与炎症和免疫反应，从而参与人类健康和疾病。 因此，生物医学研究人员对研究人类微生物组有相当大的兴趣。 由于微生物形成一个生态系统，因此可能是相互依赖的，有相当大的兴趣来描述它们之间的关联。 标准方法，如皮尔逊相关性是无效的，因为观察到的数据是组成的，即，人们只能测量特定生态系统中各种微生物的相对丰度，例如粪便样本。在这个项目中，一个正式的统计方法来估计相关性。使用婴儿肠道微生物组数据说明了所得方法。婴儿的肠道生态在出生后的第一年不断演变，由于各种因素的变化，如喂养，睡眠模式，暴露于人等。使用这种新的方法，在文献中首次，我们描述了婴儿肠道微生物群之间的关联在出生后的第一年的不同时间点。该论文发表在Nature Communications（Lin，Eggesbo和Peddada，Nature Communications，2022）上。 一对组的微生物组差异丰度分析方法在文献中得到了很好的建立。然而，许多微生物组研究涉及多个组，有时甚至是有序的组，例如疾病的阶段，并且需要不同类型的比较。标准的成对比较不仅在功效和错误发现率方面效率低下，而且它们可能无法解决感兴趣的科学问题。在该项目中，开发了一个通用框架，用于进行广泛的多组分析，并进行协变量调整和重复测量。使用两个真实的数据集说明了由此产生的方法。第一个例子探讨了干旱对土壤微生物组的影响，第二个例子研究了手术干预对IBD患者微生物组的影响。手稿正在审阅中。 在许多应用中，研究人员对组成的多变量结果感兴趣，即，观察到的数据总和为常数。例如，一个孩子在24小时内的活动。然而，由于各种原因，包括数据收集方法，有时并非所有变量都被测量，因此我们在多元组成向量中存在缺失值。假设缺失与协变量相关，本项目开发了一种简单的多重插补方法，称为成分数据的多重插补（MICoDa），以插补成分向量中的缺失值。MICoDa使用两种完全不同的数据类型进行说明，其中缺失值的出现是出于不同的原因。第一个例子涉及幼儿的24小时身体活动数据，第二个例子涉及肠道微生物组数据。这份手稿正在印刷中，将作为特邀书的一章出版。 线性回归通常用于执行中介分析。然而，在许多情况下，潜在的关系可能不是线性的，如胎盘-胎儿激素和胎儿发育的情况。此外，该关系的确切函数形式通常是未知的。出于这些原因，我们开发了一种新的基于形状限制的推理方法进行调解分析。这项工作的动机是在胎儿内分泌学的应用，研究人员有兴趣了解农药应用对出生体重的影响，与人绒毛膜促性腺激素（hCG）作为介质。我们假设hCG对出生体重的一组实际上合理的非线性影响以及农药暴露与hCG之间的线性关系，暴露-结果模型和暴露-介体模型在混杂因素中均为线性。使用所提出的方法对人口水平的产前筛查计划的数据，与hCG作为中介，我们发现，虽然自然的直接影响表明农药的应用和出生体重之间的正相关，自然的间接影响是负面的。