Annals of Applied Statistics最新文献_第8页

LATENT SUBGROUP IDENTIFICATION IN IMAGE-ON-SCALAR REGRESSION. 图像标度回归中的潜在子群识别。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2024-03-01 Epub Date: 2024-01-31 DOI: 10.1214/23-aoas1797

Zikai Lin, Yajuan Si, Jian Kang

{"title":"LATENT SUBGROUP IDENTIFICATION IN IMAGE-ON-SCALAR REGRESSION.","authors":"Zikai Lin, Yajuan Si, Jian Kang","doi":"10.1214/23-aoas1797","DOIUrl":"10.1214/23-aoas1797","url":null,"abstract":"Image-on-scalar regression has been a popular approach to modeling the association between brain activities and scalar characteristics in neuroimaging research. The associations could be heterogeneous across individuals in the population, as indicated by recent large-scale neuroimaging studies, for example, the Adolescent Brain Cognitive Development (ABCD) Study. The ABCD data can inform our understanding of heterogeneous associations and how to leverage the heterogeneity and tailor interventions to increase the number of youths who benefit. It is of great interest to identify subgroups of individuals from the population such that: (1) within each subgroup the brain activities have homogeneous associations with the clinical measures; (2) across subgroups the associations are heterogeneous, and (3) the group allocation depends on individual characteristics. Existing image-on-scalar regression methods and clustering methods cannot directly achieve this goal. We propose a latent subgroup image-on-scalar regression model (LASIR) to analyze large-scale, multisite neuroimaging data with diverse sociode-mographics. LASIR introduces the latent subgroup for each individual and group-specific, spatially varying effects, with an efficient stochastic expectation maximization algorithm for inferences. We demonstrate that LASIR outperforms existing alternatives for subgroup identification of brain activation patterns with functional magnetic resonance imaging data via comprehensive simulations and applications to the ABCD study. We have released our reproducible codes for public use with the software package available on Github.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 1","pages":"468-486"},"PeriodicalIF":1.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11156244/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141285216","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS. 用于实验中复制的非平稳时间序列的 anopow。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2024-03-01 Epub Date: 2024-01-31 DOI: 10.1214/23-aoas1791

Zeda Li, Yu Ryan Yue, Scott A Bruce

{"title":"ANOPOW FOR REPLICATED NONSTATIONARY TIME SERIES IN EXPERIMENTS.","authors":"Zeda Li, Yu Ryan Yue, Scott A Bruce","doi":"10.1214/23-aoas1791","DOIUrl":"10.1214/23-aoas1791","url":null,"abstract":"We propose a novel analysis of power (ANOPOW) model for analyzing replicated nonstationary time series commonly encountered in experimental studies. Based on a locally stationary ANOPOW Cramér spectral representation, the proposed model can be used to compare the second-order time-varying frequency patterns among different groups of time series and to estimate group effects as functions of both time and frequency. Formulated in a Bayesian framework, independent two-dimensional second-order random walk (RW2D) priors are assumed on each of the time-varying functional effects for flexible and adaptive smoothing. A piecewise stationary approximation of the nonstationary time series is used to obtain localized estimates of time-varying spectra. Posterior distributions of the time-varying functional group effects are then obtained via integrated nested Laplace approximations (INLA) at a low computational cost. The large-sample distribution of local periodograms can be appropriately utilized to improve estimation accuracy since INLA allows modeling of data with various types of distributions. The usefulness of the proposed model is illustrated through two real data applications: analyses of seismic signals and pupil diameter time series in children with attention deficit hyperactivity disorder. Simulation studies, Supplementary Materials (Li, Yue and Bruce, 2023a), and R code (Li, Yue and Bruce, 2023b) for this article are also available.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 1","pages":"328-349"},"PeriodicalIF":1.8,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10906746/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140023131","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LAND-USE FILTERING FOR NONSTATIONARY SPATIAL PREDICTION OF COLLECTIVE EFFICACY IN AN URBAN ENVIRONMENT. 利用土地利用滤波技术对城市环境中的集体效能进行非稳态空间预测。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2024-03-01 Epub Date: 2024-01-31 DOI: 10.1214/23-aoas1813

J Brandon Carter, Christopher R Browning, Bethany Boettner, Nicolo Pinchak, Catherine A Calder

{"title":"LAND-USE FILTERING FOR NONSTATIONARY SPATIAL PREDICTION OF COLLECTIVE EFFICACY IN AN URBAN ENVIRONMENT.","authors":"J Brandon Carter, Christopher R Browning, Bethany Boettner, Nicolo Pinchak, Catherine A Calder","doi":"10.1214/23-aoas1813","DOIUrl":"10.1214/23-aoas1813","url":null,"abstract":"Collective efficacy-the capacity of communities to exert social control toward the realization of their shared goals-is a foundational concept in the urban sociology and neighborhood effects literature. Traditionally, empirical studies of collective efficacy use large sample surveys to estimate collective efficacy of different neighborhoods within an urban setting. Such studies have demonstrated an association between collective efficacy and local variation in community violence, educational achievement, and health. Unlike traditional collective efficacy measurement strategies, the Adolescent Health and Development in Context (AHDC) Study implemented a new approach, obtaining spatially-referenced, place-based ratings of collective efficacy from a representative sample of individuals residing in Columbus, OH. In this paper we introduce a novel nonstationary spatial model for interpolation of the AHDC collective efficacy ratings across the study area, which leverages administrative data on land use. Our constructive model specification strategy involves dimension expansion of a latent spatial process and the use of a filter defined by the land-use partition of the study region to connect the latent multivariate spatial process to the observed ordinal ratings of collective efficacy. Careful consideration is given to the issues of parameter identifiability, computational efficiency of an MCMC algorithm for model fitting, and fine-scale spatial prediction of collective efficacy.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 1","pages":"794-818"},"PeriodicalIF":1.5,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11146085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141238803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION. 纵向二元性状的回顾性变化系数关联分析：应用于确定与高血压相关的遗传位点。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-03-01 Epub Date: 2024-01-31 DOI: 10.1214/23-aoas1798

Gang Xu, Amei Amei, Weimiao Wu, Yunqing Liu, Linchuan Shen, Edwin C Oh, Zuoheng Wang

{"title":"RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.","authors":"Gang Xu, Amei Amei, Weimiao Wu, Yunqing Liu, Linchuan Shen, Edwin C Oh, Zuoheng Wang","doi":"10.1214/23-aoas1798","DOIUrl":"10.1214/23-aoas1798","url":null,"abstract":"Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 1","pages":"487-505"},"PeriodicalIF":1.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10994004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140868741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

COMPOSITE SCORES FOR TRANSPLANT CENTER EVALUATION: A NEW INDIVIDUALIZED EMPIRICAL NULL METHOD. 用于移植中心评估的综合评分：一种新的个性化经验无效法。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2024-03-01 Epub Date: 2024-01-31 DOI: 10.1214/23-aoas1809

Nicholas Hartman, Joseph M Messana, Jian Kang, Abhijit S Naik, Tempie H Shearon, Kevin He

{"title":"COMPOSITE SCORES FOR TRANSPLANT CENTER EVALUATION: A NEW INDIVIDUALIZED EMPIRICAL NULL METHOD.","authors":"Nicholas Hartman, Joseph M Messana, Jian Kang, Abhijit S Naik, Tempie H Shearon, Kevin He","doi":"10.1214/23-aoas1809","DOIUrl":"10.1214/23-aoas1809","url":null,"abstract":"Risk-adjusted quality measures are used to evaluate healthcare providers with respect to national norms while controlling for factors beyond their control. Existing healthcare provider profiling approaches typically assume that the between-provider variation in these measures is entirely due to meaningful differences in quality of care. However, in practice, much of the between-provider variation will be due to trivial fluctuations in healthcare quality, or unobservable confounding risk factors. If these additional sources of variation are not accounted for, conventional methods will disproportionately identify larger providers as outliers, even though their departures from the national norms may not be \"extreme\" or clinically meaningful. Motivated by efforts to evaluate the quality of care provided by transplant centers, we develop a composite evaluation score based on a novel individualized empirical null method, which robustly accounts for overdispersion due to unobserved risk factors, models the marginal variance of standardized scores as a function of the effective sample size, and only requires the use of publicly-available center-level statistics. The evaluations of United States kidney transplant centers based on the proposed composite score are substantially different from those based on conventional methods. Simulations show that the proposed empirical null approach more accurately classifies centers in terms of quality of care, compared to existing methods.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"18 1","pages":"729-748"},"PeriodicalIF":1.3,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11395314/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142300086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS. 体育活动模式纵向变化的里曼流形模型框架。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-12-01 Epub Date: 2023-10-30 DOI: 10.1214/23-aoas1758

Jingjing Zou, Tuo Lin, Chongzhi Di, John Bellettiere, Marta M Jankowska, Sheri J Hartman, Dorothy D Sears, Andrea Z LaCroix, Cheryl L Rock, Loki Natarajan

{"title":"A RIEMANN MANIFOLD MODEL FRAMEWORK FOR LONGITUDINAL CHANGES IN PHYSICAL ACTIVITY PATTERNS.","authors":"Jingjing Zou, Tuo Lin, Chongzhi Di, John Bellettiere, Marta M Jankowska, Sheri J Hartman, Dorothy D Sears, Andrea Z LaCroix, Cheryl L Rock, Loki Natarajan","doi":"10.1214/23-aoas1758","DOIUrl":"10.1214/23-aoas1758","url":null,"abstract":"Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations, and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes, including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 4","pages":"3216-3240"},"PeriodicalIF":1.8,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11149895/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141249006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Binned multinomial logistic regression for integrative cell-type annotation. 综合细胞类型标注的分类多项式逻辑回归。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2023-12-01 DOI: 10.1214/23-aoas1769

Keshav Motwani, Rhonda Bacher, Aaron J Molstad

{"title":"Binned multinomial logistic regression for integrative cell-type annotation.","authors":"Keshav Motwani, Rhonda Bacher, Aaron J Molstad","doi":"10.1214/23-aoas1769","DOIUrl":"https://doi.org/10.1214/23-aoas1769","url":null,"abstract":"Categorizing individual cells into one of many known cell type categories, also known as cell type annotation, is a critical step in the analysis of single-cell genomics data. The current process of annotation is time-intensive and subjective, which has led to different studies describing cell types with labels of varying degrees of resolution. While supervised learning approaches have provided automated solutions to annotation, there remains a significant challenge in fitting a unified model for multiple datasets with inconsistent labels. In this article, we propose a new multinomial logistic regression estimator which can be used to model cell type probabilities by integrating multiple datasets with labels of varying resolution. To compute our estimator, we solve a nonconvex optimization problem using a blockwise proximal gradient descent algorithm. We show through simulation studies that our approach estimates cell type probabilities more accurately than competitors in a wide variety of scenarios. We apply our method to ten single-cell RNA-seq datasets and demonstrate its utility in predicting fine resolution cell type labels on unlabeled data as well as refining cell type labels on data with existing coarse resolution annotations. Finally, we demonstrate that our method can lead to novel scientific insights in the context of a differential expression analysis comparing peripheral blood gene expression before and after treatment with interferon- <math><mi>β</mi></math> . An R package implementing the method is available at https://github.com/keshav-motwani/IBMR and the collection of datasets we analyze is available at https://github.com/keshav-motwani/AnnotatedPBMC.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 4","pages":"3426-3449"},"PeriodicalIF":1.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11981643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143993314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PAIRWISE NONLINEAR DEPENDENCE ANALYSIS OF GENOMIC DATA. 基因组数据的两两非线性相关性分析。

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2023-12-01 Epub Date: 2023-10-30 DOI: 10.1214/23-aoas1745

Siqi Xiang, Wan Zhang, Siyao Liu, Katherine A Hoadley, Charles M Perou, Kai Zhang, J S Marron

引用次数: 0

TARGETING UNDERREPRESENTED POPULATIONS IN PRECISION MEDICINE: A FEDERATED TRANSFER LEARNING APPROACH. 针对精准医疗中代表性不足的人群：一种联合转移学习方法。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2023-12-01 Epub Date: 2023-10-30 DOI: 10.1214/23-AOAS1747

By Sai Li, Tianxi Cai, Rui Duan

{"title":"TARGETING UNDERREPRESENTED POPULATIONS IN PRECISION MEDICINE: A FEDERATED TRANSFER LEARNING APPROACH.","authors":"By Sai Li, Tianxi Cai, Rui Duan","doi":"10.1214/23-AOAS1747","DOIUrl":"10.1214/23-AOAS1747","url":null,"abstract":"The limited representation of minorities and disadvantaged populations in large-scale clinical and genomics research poses a significant barrier to translating precision medicine research into practice. Prediction models are likely to underperform in underrepresented populations due to heterogeneity across populations, thereby exacerbating known health disparities. To address this issue, we propose FETA, a two-way data integration method that leverages a federated transfer learning approach to integrate heterogeneous data from diverse populations and multiple healthcare institutions, with a focus on a target population of interest having limited sample sizes. We show that FETA achieves performance comparable to the pooled analysis, where individual-level data is shared across institutions, with only a small number of communications across participating sites. Our theoretical analysis and simulation study demonstrate how FETA's estimation accuracy is influenced by communication budgets, privacy restrictions, and heterogeneity across populations. We apply FETA to multisite data from the electronic Medical Records and Genomics (eMERGE) Network to construct genetic risk prediction models for extreme obesity. Compared to models trained using target data only, source data only, and all data without accounting for population-level differences, FETA shows superior predictive performance. FETA has the potential to improve estimation and prediction accuracy in underrepresented populations and reduce the gap in model performance across populations.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 4","pages":"2970-2992"},"PeriodicalIF":1.3,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11417462/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142309007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

MODELING RACIAL/ETHNIC DIFFERENCES IN COVID-19 INCIDENCE WITH COVARIATES SUBJECT TO NON-RANDOM MISSINGNESS. 用非随机缺失的协变量对COVID-19发病率的种族/民族差异进行建模

IF 1.5 4区数学

Annals of Applied Statistics Pub Date : 2023-12-01 DOI: 10.1214/22-aoas1711

Rob Trangucci, Yang Chen, Jon Zelner

{"title":"MODELING RACIAL/ETHNIC DIFFERENCES IN COVID-19 INCIDENCE WITH COVARIATES SUBJECT TO NON-RANDOM MISSINGNESS.","authors":"Rob Trangucci, Yang Chen, Jon Zelner","doi":"10.1214/22-aoas1711","DOIUrl":"10.1214/22-aoas1711","url":null,"abstract":"Characterizing the cumulative burden of COVID-19 by race/ethnicity is of the utmost importance for public health researchers and policy makers in order to design effective mitigation measures. This analysis is hampered, however, by surveillance case data with substantial missingness in race and ethnicity covariates. Worse yet, this missingness likely depends on the values of these missing covariates, i.e. they are not missing at random (NMAR). We propose a Bayesian parametric model that leverages joint information on spatial variation in the disease and covariate missingness processes and can accommodate both MAR and NMAR missingness. We show that the model is locally identifiable when the spatial distribution of the population covariates is known and observed cases can be associated with a spatial unit of observation. We also use a simulation study to investigate the model's finite-sample performance. We compare our model's performance on NMAR data against complete-case analysis and multiple imputation (MI), both of which are commonly used by public health researchers when confronted with missing categorical covariates. Finally, we model spatial variation in cumulative COVID-19 incidence in Wayne County, Michigan using data from the Michigan Department and Health and Human Services. The analysis suggests that population relative risk estimates by race during the early part of the COVID-19 pandemic in Michigan were understated for non-white residents compared to white residents when cases missing race were dropped or had these values imputed using MI.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 4","pages":"2723-2758"},"PeriodicalIF":1.5,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13591633/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149017901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0