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EXPOSURE EFFECTS ON COUNT OUTCOMES WITH OBSERVATIONAL DATA, WITH APPLICATION TO INCARCERATED WOMEN. 通过观察数据分析暴露对计数结果的影响,并将其应用于被监禁妇女。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-09-01 Epub Date: 2024-08-05 DOI: 10.1214/24-aoas1874
Bonnie E Shook-Sa, Michael G Hudgens, Andrea K Knittel, Andrew Edmonds, Catalina Ramirez, Stephen R Cole, Mardge Cohen, Adebola Adedimeji, Tonya Taylor, Katherine G Michel, Andrea Kovacs, Jennifer Cohen, Jessica Donohue, Antonina Foster, Margaret A Fischl, Dustin Long, Adaora A Adimora

Causal inference methods can be applied to estimate the effect of a point exposure or treatment on an outcome of interest using data from observational studies. For example, in the Women's Interagency HIV Study, it is of interest to understand the effects of incarceration on the number of sexual partners and the number of cigarettes smoked after incarceration. In settings like this where the outcome is a count, the estimand is often the causal mean ratio, i.e., the ratio of the counterfactual mean count under exposure to the counterfactual mean count under no exposure. This paper considers estimators of the causal mean ratio based on inverse probability of treatment weights, the parametric g-formula, and doubly robust estimation, each of which can account for overdispersion, zero-inflation, and heaping in the measured outcome. Methods are compared in simulations and are applied to data from the Women's Interagency HIV Study.

因果推理方法可用于利用观察性研究的数据估算点暴露或治疗对相关结果的影响。例如,在 "妇女机构间艾滋病研究"(Women's Interagency HIV Study)中,我们有兴趣了解监禁对监禁后性伴侣数量和吸烟数量的影响。在这种结果为计数的情况下,估计值通常为因果平均比率,即暴露情况下的反事实平均计数与不暴露情况下的反事实平均计数之比。本文考虑了基于逆概率处理权重、参数 g 公式和双重稳健估计的因果平均比率估计方法,每种方法都可以考虑测量结果中的过度分散、零膨胀和堆叠。通过模拟对这些方法进行了比较,并将其应用于妇女机构间艾滋病毒研究的数据中。
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引用次数: 0
BIVARIATE FUNCTIONAL PATTERNS OF LIFETIME MEDICARE COSTS AMONG ESRD PATIENTS. ESD 患者终身医疗保险费用的双变量功能模式。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-09-01 Epub Date: 2024-08-05 DOI: 10.1214/24-aoas1897
Yue Wang, Bin Nan, John D Kalbfleisch

In this work we study the lifetime Medicare spending patterns of patients with end-stage renal disease (ESRD). We extract the information of patients who started their ESRD services in 2007-2011 from the United States Renal Data System (USRDS). Patients are partitioned into three groups based on their kidney transplant status: 1-unwaitlisted and never transplanted, 2-waitlisted but never transplanted, and 3-waitlisted and then transplanted. To study their Medicare cost trajectories, we use a semiparametric regression model with both fixed and bivariate time-varying coefficients to compare groups 1 and 2, and a bivariate time-varying coefficient model with different starting times (time since the first ESRD service and time since the kidney transplant) to compare groups 2 and 3. In addition to demographics and other medical conditions, these regression models are conditional on the survival time, which ideally depict the lifetime Medicare spending patterns. For estimation, we extend the profile weighted least squares (PWLS) estimator to longitudinal data for the first comparison and propose a two-stage estimating method for the second comparison. We use sandwich variance estimators to construct confidence intervals and validate inference procedures through simulations. Our analysis of the Medicare claims data reveals that waitlisting is associated with a lower daily medical cost at the beginning of ESRD service among waitlisted patients which gradually increases over time. Averaging over lifespan, however, there is no difference between waitlisted and unwaitlisted groups. A kidney transplant, on the other hand, reduces the medical cost significantly after an initial spike.

在这项工作中,我们研究了终末期肾病(ESRD)患者的终生医疗保险支出模式。我们从美国肾脏数据系统(USRDS)中提取了 2007-2011 年开始接受 ESRD 服务的患者信息。根据患者的肾移植状态将其分为三组:1-未列入等待名单且从未移植;2-列入等待名单但从未移植;3-列入等待名单后移植。为了研究他们的医疗保险费用轨迹,我们使用了一个具有固定系数和双变量时变系数的半参数回归模型来比较第 1 组和第 2 组,以及一个具有不同起始时间(首次 ESRD 服务起始时间和肾移植起始时间)的双变量时变系数模型来比较第 2 组和第 3 组。除人口统计学和其他医疗条件外,这些回归模型还以生存时间为条件,从而理想地描绘出医疗保险的终生支出模式。在估算时,我们将剖面加权最小二乘法(PWLS)估算器扩展到纵向数据,用于第一组比较,并为第二组比较提出了两阶段估算方法。我们使用三明治方差估计器构建置信区间,并通过模拟验证推断程序。我们对医疗保险理赔数据的分析表明,在 ESRD 服务开始时,候补患者的每日医疗费用较低,而随着时间的推移,这一费用会逐渐增加。然而,从生命周期的平均值来看,候诊组和未候诊组之间并无差异。另一方面,肾移植在最初的峰值之后会显著降低医疗费用。
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引用次数: 0
A bootstrap model comparison test for identifying genes with context-specific patterns of genetic regulation. 用于识别具有特定遗传调控模式的基因的自举模型比较检验。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-09-01 Epub Date: 2024-08-05 DOI: 10.1214/23-aoas1859
Mykhaylo M Malakhov, Ben Dai, Xiaotong T Shen, Wei Pan

Understanding how genetic variation affects gene expression is essential for a complete picture of the functional pathways that give rise to complex traits. Although numerous studies have established that many genes are differentially expressed in distinct human tissues and cell types, no tools exist for identifying the genes whose expression is differentially regulated. Here we introduce DRAB (differential regulation analysis by bootstrapping), a gene-based method for testing whether patterns of genetic regulation are significantly different between tissues or other biological contexts. DRAB first leverages the elastic net to learn context-specific models of local genetic regulation and then applies a novel bootstrap-based model comparison test to check their equivalency. Unlike previous model comparison tests, our proposed approach can determine whether population-level models have equal predictive performance by accounting for the variability of feature selection and model training. We validated DRAB on mRNA expression data from a variety of human tissues in the Genotype-Tissue Expression (GTEx) Project. DRAB yielded biologically reasonable results and had sufficient power to detect genes with tissue-specific regulatory profiles while effectively controlling false positives. By providing a framework that facilitates the prioritization of differentially regulated genes, our study enables future discoveries on the genetic architecture of molecular phenotypes.

要全面了解导致复杂性状的功能途径,就必须了解遗传变异是如何影响基因表达的。尽管大量研究已经证实,许多基因在不同的人体组织和细胞类型中表达不同,但目前还没有工具可以识别表达受到不同调控的基因。在这里,我们介绍一种基于基因的方法--DRAB(自举法差异调控分析),用于测试不同组织或其他生物环境中的基因调控模式是否存在显著差异。DRAB 首先利用弹性网来学习局部基因调控的特定背景模型,然后应用一种新颖的基于引导的模型比较测试来检验它们的等效性。与以往的模型比较测试不同,我们提出的方法可以通过考虑特征选择和模型训练的可变性来确定群体级模型是否具有相同的预测性能。我们在基因型-组织表达(GTEx)项目中对来自各种人体组织的 mRNA 表达数据进行了 DRAB 验证。DRAB 得出了生物学上合理的结果,并有足够的能力检测出具有组织特异性调控特征的基因,同时有效控制了假阳性。我们的研究提供了一个框架,有助于确定差异调控基因的优先次序,从而有助于未来发现分子表型的遗传结构。
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引用次数: 0
PATIENT RECRUITMENT USING ELECTRONIC HEALTH RECORDS UNDER SELECTION BIAS: A TWO-PHASE SAMPLING FRAMEWORK. 在选择偏差的情况下利用电子病历招募患者:两阶段抽样框架。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-09-01 Epub Date: 2024-08-05 DOI: 10.1214/23-aoas1860
Guanghao Zhang, Lauren J Beesley, Bhramar Mukherjee, X U Shi

Electronic health records (EHRs) are increasingly recognized as a cost-effective resource for patient recruitment in clinical research. However, how to optimally select a cohort from millions of individuals to answer a scientific question of interest remains unclear. Consider a study to estimate the mean or mean difference of an expensive outcome. Inexpensive auxiliary covariates predictive of the outcome may often be available in patients' health records, presenting an opportunity to recruit patients selectively, which may improve efficiency in downstream analyses. In this paper we propose a two-phase sampling design that leverages available information on auxiliary covariates in EHR data. A key challenge in using EHR data for multiphase sampling is the potential selection bias, because EHR data are not necessarily representative of the target population. Extending existing literature on two-phase sampling design, we derive an optimal two-phase sampling method that improves efficiency over random sampling while accounting for the potential selection bias in EHR data. We demonstrate the efficiency gain from our sampling design via simulation studies and an application evaluating the prevalence of hypertension among U.S. adults leveraging data from the Michigan Genomics Initiative, a longitudinal biorepository in Michigan Medicine.

电子健康记录(EHR)越来越被认为是临床研究中招募病人的一种具有成本效益的资源。然而,如何从数以百万计的个体中最优化地选择一个队列来回答感兴趣的科学问题仍不清楚。考虑一项估算昂贵结果的平均值或平均差的研究。患者的健康记录中通常可能存在可预测结果的廉价辅助协变量,这为有选择性地招募患者提供了机会,可提高下游分析的效率。在本文中,我们提出了一种两阶段抽样设计,充分利用电子病历数据中可用的辅助协变量信息。使用电子病历数据进行多阶段抽样的一个主要挑战是潜在的选择偏差,因为电子病历数据并不一定代表目标人群。我们扩展了有关两阶段抽样设计的现有文献,推导出了一种最佳的两阶段抽样方法,它比随机抽样提高了效率,同时考虑到了电子病历数据中潜在的选择偏差。我们通过模拟研究和一个评估美国成年人高血压患病率的应用,利用密歇根基因组学倡议(Michigan Genomics Initiative)的数据(密歇根医学的一个纵向生物库),证明了我们的抽样设计提高了效率。
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引用次数: 0
A NONPARAMETRIC MIXED-EFFECTS MIXTURE MODEL FOR PATTERNS OF CLINICAL MEASUREMENTS ASSOCIATED WITH COVID-19. 与 covid-19 相关的临床测量模式的非参数混合效应混合物模型。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-09-01 Epub Date: 2024-08-05 DOI: 10.1214/23-aoas1871
Xiaoran Ma, Wensheng Guo, Mengyang Gu, Len Usvyat, Peter Kotanko, Yuedong Wang

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insights into how the immune system may respond differently to infection and can further be used to identify infected individuals. We propose a flexible nonparametric mixed-effects mixture model that identifies risk factors and classifies patients with biological changes. We model the latent probability of biological changes using a logistic regression model and trajectories in the latent groups using smoothing splines. We developed an EM algorithm to maximize the penalized likelihood for estimating all parameters and mean functions. We evaluate our methods by simulations and apply the proposed model to investigate changes in temperature in a cohort of COVID-19-infected hemodialysis patients.

一些 COVID-19 患者在接受 SARS-CoV-2 阳性检测前几天体温和血氧饱和度等体征和症状发生变化,而另一些患者则仍无症状。确定这些亚群并了解与这些亚群相关的生物学和临床预测因素非常重要。这些信息将有助于了解免疫系统如何对感染做出不同的反应,并可进一步用于识别感染者。我们提出了一种灵活的非参数混合效应模型,该模型可识别风险因素,并根据生物变化对患者进行分类。我们使用逻辑回归模型对生物变化的潜伏概率进行建模,并使用平滑样条对潜伏组的轨迹进行建模。我们开发了一种 EM 算法,用于最大化估计所有参数和均值函数的惩罚似然。我们通过模拟评估了我们的方法,并将所提出的模型应用于研究 COVID-19 感染血液透析患者队列中的体温变化。
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引用次数: 0
JOINT MODELING OF MULTISTATE AND NONPARAMETRIC MULTIVARIATE LONGITUDINAL DATA. 多态和非参数多变量纵向数据的联合建模。
IF 1.8 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-08-05 DOI: 10.1214/24-aoas1889
L U You,Falastin Salami,Carina Törn,Åke Lernmark,Roy Tamura
It is oftentimes the case in studies of disease progression that subjects can move into one of several disease states of interest. Multistate models are an indispensable tool to analyze data from such studies. The Environmental Determinants of Diabetes in the Young (TEDDY) is an observational study of at-risk children from birth to onset of type-1 diabetes (T1D) up through the age of 15. A joint model for simultaneous inference of multistate and multivariate nonparametric longitudinal data is proposed to analyze data and answer the research questions brought up in the study. The proposed method allows us to make statistical inferences, test hypotheses, and make predictions about future state occupation in the TEDDY study. The performance of the proposed method is evaluated by simulation studies. The proposed method is applied to the motivating example to demonstrate the capabilities of the method.
在疾病进展研究中,受试者往往会进入几种相关疾病状态中的一种。多态模型是分析此类研究数据不可或缺的工具。青少年糖尿病的环境决定因素(TEDDY)是一项观察性研究,研究对象为从出生到 1 型糖尿病(T1D)发病直至 15 岁的高危儿童。本研究提出了一种多态和多变量非参数纵向数据同时推断的联合模型,用于分析数据和回答研究中提出的问题。通过所提出的方法,我们可以在 TEDDY 研究中进行统计推断、检验假设并预测未来的职业状态。我们通过模拟研究对所提方法的性能进行了评估。建议的方法应用于激励性实例,以展示该方法的能力。
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引用次数: 0
BAYESIAN NESTED LATENT CLASS MODELS FOR CAUSE-OF-DEATH ASSIGNMENT USING VERBAL AUTOPSIES ACROSS MULTIPLE DOMAINS. 利用多领域口头尸检的贝叶斯嵌套潜类模型确定死因。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-06-01 Epub Date: 2024-04-05 DOI: 10.1214/23-aoas1826
Zehang Richard Li, Zhenke Wu, Irena Chen, Samuel J Clark

Understanding cause-specific mortality rates is crucial for monitoring population health and designing public health interventions. Worldwide, two-thirds of deaths do not have a cause assigned. Verbal autopsy (VA) is a well-established tool to collect information describing deaths outside of hospitals by conducting surveys to caregivers of a deceased person. It is routinely implemented in many low- and middle-income countries. Statistical algorithms to assign cause of death using VAs are typically vulnerable to the distribution shift between the data used to train the model and the target population. This presents a major challenge for analyzing VAs, as labeled data are usually unavailable in the target population. This article proposes a latent class model framework for VA data (LCVA) that jointly models VAs collected over multiple heterogeneous domains, assigns causes of death for out-of-domain observations and estimates cause-specific mortality fractions for a new domain. We introduce a parsimonious representation of the joint distribution of the collected symptoms using nested latent class models and develop a computationally efficient algorithm for posterior inference. We demonstrate that LCVA outperforms existing methods in predictive performance and scalability. Supplementary Material and reproducible analysis codes are available online. The R package LCVA implementing the method is available on GitHub (https://github.com/richardli/LCVA).

了解特定病因死亡率对于监测人口健康和设计公共卫生干预措施至关重要。在世界范围内,三分之二的死亡没有指定死因。口头尸检(VA)是一种行之有效的工具,通过对死者的护理人员进行调查,收集医院外的死亡信息。在许多中低收入国家,这种方法已成为常规。使用尸体解剖确定死因的统计算法通常容易受到用于训练模型的数据与目标人群之间分布变化的影响。由于目标人群中通常没有标注数据,这给分析 VAs 带来了重大挑战。本文提出了一种针对退伍军人数据的潜类模型框架(LCVA),该框架可对多个异质领域收集的退伍军人数据进行联合建模,为领域外观测数据指定死因,并估算新领域的特定死因死亡率分数。我们使用嵌套潜类模型对收集到的症状的联合分布进行了简明表述,并开发了一种计算高效的后验推断算法。我们证明 LCVA 在预测性能和可扩展性方面优于现有方法。补充材料和可重复的分析代码可在线获取。实现该方法的 R 软件包 LCVA 可在 GitHub 上获取 (https://github.com/richardli/LCVA)。
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引用次数: 0
TENSOR QUANTILE REGRESSION WITH LOW-RANK TENSOR TRAIN ESTIMATION. 张量量子回归与低等级张量列车估计。
IF 1.8 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-06-01 Epub Date: 2024-04-05 DOI: 10.1214/23-aoas1835
Zihuan Liu, Cheuk Yin Lee, Heping Zhang

Neuroimaging studies often involve predicting a scalar outcome from an array of images collectively called tensor. The use of magnetic resonance imaging (MRI) provides a unique opportunity to investigate the structures of the brain. To learn the association between MRI images and human intelligence, we formulate a scalar-on-image quantile regression framework. However, the high dimensionality of the tensor makes estimating the coefficients for all elements computationally challenging. To address this, we propose a low-rank coefficient array estimation algorithm based on tensor train (TT) decomposition which we demonstrate can effectively reduce the dimensionality of the coefficient tensor to a feasible level while ensuring adequacy to the data. Our method is more stable and efficient compared to the commonly used, Canonic Polyadic rank approximation-based method. We also propose a generalized Lasso penalty on the coefficient tensor to take advantage of the spatial structure of the tensor, further reduce the dimensionality of the coefficient tensor, and improve the interpretability of the model. The consistency and asymptotic normality of the TT estimator are established under some mild conditions on the covariates and random errors in quantile regression models. The rate of convergence is obtained with regularization under the total variation penalty. Extensive numerical studies, including both synthetic and real MRI imaging data, are conducted to examine the empirical performance of the proposed method and its competitors.

神经成像研究通常涉及从统称为张量的图像阵列中预测标量结果。磁共振成像(MRI)的使用为研究大脑结构提供了独特的机会。为了了解核磁共振成像图像与人类智力之间的关联,我们制定了一个图像标量量化回归框架。然而,张量的高维度使得估算所有元素的系数在计算上具有挑战性。为了解决这个问题,我们提出了一种基于张量列车(TT)分解的低秩系数阵列估计算法,我们证明这种算法可以有效地将系数张量的维度降低到可行的水平,同时确保数据的充分性。与常用的基于卡诺尼多模秩近似的方法相比,我们的方法更稳定、更高效。我们还提出了对系数张量的广义 Lasso 惩罚,以利用张量的空间结构,进一步降低系数张量的维度,提高模型的可解释性。在量化回归模型的协变量和随机误差的一些温和条件下,建立了 TT 估计器的一致性和渐近正态性。在总变异惩罚下,通过正则化获得了收敛率。我们还进行了广泛的数值研究,包括合成和真实的核磁共振成像数据,以检验所提出的方法及其竞争对手的经验性能。
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引用次数: 0
MASH: MEDIATION ANALYSIS OF SURVIVAL OUTCOME AND HIGH-DIMENSIONAL OMICS MEDIATORS WITH APPLICATION TO COMPLEX DISEASES. mash:生存结果和高维 omics 中介因子的中介分析,适用于复杂疾病。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-06-01 Epub Date: 2024-04-05 DOI: 10.1214/23-aoas1838
Sunyi Chi, Christopher R Flowers, Ziyi Li, Xuelin Huang, Peng Wei

Environmental exposures such as cigarette smoking influence health outcomes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for investigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. However, little work has been done on mediation analysis when the mediators are high-dimensional and the outcome is a survival endpoint, and none of it has provided a robust measure of total mediation effect. To this end, we propose an estimation procedure for Mediation Analysis of Survival outcome and High-dimensional omics mediators (MASH) based on sure independence screening for putative mediator variable selection and a second-moment-based measure of total mediation effect for survival data analogous to the R 2 measure in a linear model. Extensive simulations showed good performance of MASH in estimating the total mediation effect and identifying true mediators. By applying MASH to the metabolomics data of 1919 subjects in the Framingham Heart Study, we identified five metabolites as mediators of the effect of cigarette smoking on coronary heart disease risk (total mediation effect, 51.1%) and two metabolites as mediators between smoking and risk of cancer (total mediation effect, 50.7%). Application of MASH to a diffuse large B-cell lymphoma genomics data set identified copy-number variations for eight genes as mediators between the baseline International Prognostic Index score and overall survival.

吸烟等环境暴露通过中间分子表型(如甲基组、转录组和代谢组)影响健康结果。中介分析是研究潜在高维中间表型在环境暴露与健康结果之间关系中的作用的有用工具。然而,当中介因素是高维的,而结果是生存终点时,中介分析方面的工作很少,而且没有一项工作提供了总中介效应的稳健测量方法。为此,我们提出了一种生存结果与高维 omics 中介因子中介分析(MASH)的估算程序,该程序基于对推定中介变量选择的确定独立性筛选,以及对生存数据的总中介效应的基于第二时刻的测量,类似于线性模型中的 R 2 测量。大量模拟结果表明,MASH 在估计总中介效应和识别真正的中介因子方面表现出色。通过将 MASH 应用于弗雷明汉心脏研究中 1919 名受试者的代谢组学数据,我们确定了五种代谢物是吸烟对冠心病风险影响的中介物(总中介效应为 51.1%),两种代谢物是吸烟与癌症风险之间的中介物(总中介效应为 50.7%)。将 MASH 应用于弥漫大 B 细胞淋巴瘤基因组学数据集,发现 8 个基因的拷贝数变异是基线国际预后指数评分与总生存期之间的中介因子。
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引用次数: 0
A BAYESIAN HIERARCHICAL SMALL AREA POPULATION MODEL ACCOUNTING FOR DATA SOURCE SPECIFIC METHODOLOGIES FROM AMERICAN COMMUNITY SURVEY, POPULATION ESTIMATES PROGRAM, AND DECENNIAL CENSUS DATA. 根据美国社区调查、人口估计计划和十年一次的人口普查数据,建立一个考虑到数据源特定方法的贝叶斯分层小地区人口模型。
IF 1.3 4区 数学 Q2 STATISTICS & PROBABILITY Pub Date : 2024-06-01 Epub Date: 2024-04-05 DOI: 10.1214/23-aoas1849
Emily N Peterson, Rachel C Nethery, Tullia Padellini, Jarvis T Chen, Brent A Coull, Frédéric B Piel, Jon Wakefield, Marta Blangiardo, Lance A Waller

Small area population counts are necessary for many epidemiological studies, yet their quality and accuracy are often not assessed. In the United States, small area population counts are published by the United States Census Bureau (USCB) in the form of the decennial census counts, intercensal population projections (PEP), and American Community Survey (ACS) estimates. Although there are significant relationships between these three data sources, there are important contrasts in data collection, data availability, and processing methodologies such that each set of reported population counts may be subject to different sources and magnitudes of error. Additionally, these data sources do not report identical small area population counts due to post-survey adjustments specific to each data source. Consequently, in public health studies, small area disease/mortality rates may differ depending on which data source is used for denominator data. To accurately estimate annual small area population counts and their associated uncertainties, we present a Bayesian population (BPop) model, which fuses information from all three USCB sources, accounting for data source specific methodologies and associated errors. We produce comprehensive small area race-stratified estimates of the true population, and associated uncertainties, given the observed trends in all three USCB population estimates. The main features of our framework are: (1) a single model integrating multiple data sources, (2) accounting for data source specific data generating mechanisms and specifically accounting for data source specific errors, and (3) prediction of population counts for years without USCB reported data. We focus our study on the Black and White only populations for 159 counties of Georgia and produce estimates for years 2006-2023. We compare BPop population estimates to decennial census counts, PEP annual counts, and ACS multi-year estimates. Additionally, we illustrate and explain the different types of data source specific errors. Lastly, we compare model performance using simulations and validation exercises. Our Bayesian population model can be extended to other applications at smaller spatial granularity and for demographic subpopulations defined further by race, age, and sex, and/or for other geographical regions.

小地区人口统计是许多流行病学研究的必要条件,但其质量和准确性往往得不到评估。在美国,小地区人口统计由美国人口普查局(USCB)以十年一次的人口普查计数、普查间人口预测(PEP)和美国社区调查(ACS)估计值的形式发布。虽然这三个数据源之间存在重要关系,但在数据收集、数据可用性和处理方法方面存在重要差异,因此每套报告的人口数量可能会受到不同来源和不同程度误差的影响。此外,由于每个数据源都会进行特定的调查后调整,因此这些数据源报告的小地区人口数并不完全相同。因此,在公共卫生研究中,小地区疾病/死亡率可能会因分母数据使用的数据源不同而不同。为了准确估算年度小地区人口数量及其相关的不确定性,我们提出了一个贝叶斯人口(BPop)模型,该模型融合了 USCB 所有三个来源的信息,并考虑了数据源特定的方法和相关误差。考虑到所有三个 USCB 人口估计中观察到的趋势,我们对真实人口及其相关不确定性进行了全面的小区域种族分层估计。我们的框架的主要特点是(1) 整合多个数据源的单一模型,(2) 考虑到数据源特定的数据生成机制,特别是考虑到数据源特定的误差,以及 (3) 对没有 USCB 报告数据的年份的人口数量进行预测。我们的研究重点是佐治亚州 159 个县的黑人和白人人口,并得出 2006-2023 年的估计值。我们将 BPop 人口估计值与十年一次的人口普查计数、PEP 年度计数和 ACS 多年估计值进行了比较。此外,我们还说明并解释了不同类型的数据源特定误差。最后,我们通过模拟和验证练习来比较模型的性能。我们的贝叶斯人口模型可扩展到其他应用领域,如更小的空间粒度、按种族、年龄和性别进一步定义的人口亚群,以及/或其他地理区域。
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引用次数: 0
期刊
Annals of Applied Statistics
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