Annals of Applied Statistics最新文献_第4页

BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA. 多变量纵向和生存数据的贝叶斯推理和动态预测。

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2023-09-01 Epub Date: 2023-09-07 DOI: 10.1214/23-aoas1733

Haotian Zou, Donglin Zeng, Luo Xiao, Sheng Luo

{"title":"BAYESIAN INFERENCE AND DYNAMIC PREDICTION FOR MULTIVARIATE LONGITUDINAL AND SURVIVAL DATA.","authors":"Haotian Zou, Donglin Zeng, Luo Xiao, Sheng Luo","doi":"10.1214/23-aoas1733","DOIUrl":"10.1214/23-aoas1733","url":null,"abstract":"Alzheimer's disease (AD) is a complex neurological disorder impairing multiple domains such as cognition and daily functions. To better understand the disease and its progression, many AD research studies collect multiple longitudinal outcomes that are strongly predictive of the onset of AD dementia. We propose a joint model based on a multivariate functional mixed model framework (referred to as MFMM-JM) that simultaneously models the multiple longitudinal outcomes and the time to dementia onset. We develop six functional forms to fully investigate the complex association between longitudinal outcomes and dementia onset. Moreover, we use the Bayesian methods for statistical inference and develop a dynamic prediction framework that provides accurate personalized predictions of disease progressions based on new subject-specific data. We apply the proposed MFMM-JM to two large ongoing AD studies: the Alzheimer's Disease Neuroimaging Initiative (ADNI) and National Alzheimer's Coordinating Center (NACC), and identify the functional forms with the best predictive performance. our method is also validated by extensive simulation studies with five settings.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 3","pages":"2574-2595"},"PeriodicalIF":1.3,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10500582/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10339586","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

PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER. 癌症治疗树的概率学习。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-09-01 Epub Date: 2023-09-07 DOI: 10.1214/22-aoas1696

Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li, Veerabhadran Baladandayuthapani

{"title":"PROBABILISTIC LEARNING OF TREATMENT TREES IN CANCER.","authors":"Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li, Veerabhadran Baladandayuthapani","doi":"10.1214/22-aoas1696","DOIUrl":"10.1214/22-aoas1696","url":null,"abstract":"Accurate identification of synergistic treatment combinations and their underlying biological mechanisms is critical across many disease domains, especially cancer. In translational oncology research, preclinical systems such as patient-derived xenografts (PDX) have emerged as a unique study design evaluating multiple treatments administered to samples from the same human tumor implanted into genetically identical mice. In this paper, we propose a novel Bayesian probabilistic tree-based framework for PDX data to investigate the hierarchical relationships between treatments by inferring treatment cluster trees, referred to as treatment trees (Rx-tree). The framework motivates a new metric of mechanistic similarity between two or more treatments accounting for inherent uncertainty in tree estimation; treatments with a high estimated similarity have potentially high mechanistic synergy. Building upon Dirichlet Diffusion Trees, we derive a closed-form marginal likelihood encoding the tree structure, which facilitates computationally efficient posterior inference via a new two-stage algorithm. Simulation studies demonstrate superior performance of the proposed method in recovering the tree structure and treatment similarities. Our analyses of a recently collated PDX dataset produce treatment similarity estimates that show a high degree of concordance with known biological mechanisms across treatments in five different cancers. More importantly, we uncover new and potentially effective combination therapies that confer synergistic regulation of specific downstream biological pathways for future clinical investigations. Our accompanying code, data, and shiny application for visualization of results are available at: https://github.com/bayesrx/RxTree.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 3","pages":"1884-1908"},"PeriodicalIF":1.8,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10501503/pdf/nihms-1857187.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10308161","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

IDENTIFICATION OF IMMUNE RESPONSE COMBINATIONS ASSOCIATED WITH HETEROGENEOUS INFECTION RISK IN THE IMMUNE CORRELATES ANALYSIS OF HIV VACCINE STUDIES. 在艾滋病毒疫苗研究的免疫相关性分析中确定与异质性感染风险相关的免疫反应组合。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-06-01 Epub Date: 2023-05-01 DOI: 10.1214/22-aoas1665

Chaeryon Kang, Ying Huang

{"title":"IDENTIFICATION OF IMMUNE RESPONSE COMBINATIONS ASSOCIATED WITH HETEROGENEOUS INFECTION RISK IN THE IMMUNE CORRELATES ANALYSIS OF HIV VACCINE STUDIES.","authors":"Chaeryon Kang, Ying Huang","doi":"10.1214/22-aoas1665","DOIUrl":"10.1214/22-aoas1665","url":null,"abstract":"In HIV vaccine/prevention research, probing into the vaccine-induced immune responses that can help predict the risk of HIV infection provides valuable information for the development of vaccine regimens. Previous correlate analysis of the Thai vaccine trial aided the discovery of interesting immune correlates related to the risk of developing an HIV infection. The present study aimed to identify the combinations of immune responses associated with the heterogeneous infection risk. We explored a \"change-plane\" via combination of a subset of immune responses that could help separate vaccine recipients into two heterogeneous subgroups in terms of the association between immune responses and the risk of developing infection. Additionally, we developed a new variable selection algorithm through a penalized likelihood approach to investigate a parsimonious marker combination for the change-plane. The resulting marker combinations can serve as candidate correlates of protection and can be used for predicting the protective effect of the vaccine against HIV infection. The application of the proposed statistical approach to the Thai trial has been presented, wherein the marker combinations were explored among several immune responses and antigens.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 2","pages":"1199-1219"},"PeriodicalIF":1.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10312353/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9755428","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

CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA. 空间分辨转录组数据的联合聚类。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-06-01 Epub Date: 2023-05-01 DOI: 10.1214/22-aoas1677

Andrea Sottosanti, Davide Risso

{"title":"CO-CLUSTERING OF SPATIALLY RESOLVED TRANSCRIPTOMIC DATA.","authors":"Andrea Sottosanti, Davide Risso","doi":"10.1214/22-aoas1677","DOIUrl":"10.1214/22-aoas1677","url":null,"abstract":"Spatial transcriptomics is a groundbreaking technology that allows the measurement of the activity of thousands of genes in a tissue sample and maps where the activity occurs. This technology has enabled the study of the spatial variation of the genes across the tissue. Comprehending gene functions and interactions in different areas of the tissue is of great scientific interest, as it might lead to a deeper understanding of several key biological mechanisms, such as cell-cell communication or tumor-microenvironment interaction. To do so, one can group cells of the same type and genes that exhibit similar expression patterns. However, adequate statistical tools that exploit the previously unavailable spatial information to more coherently group cells and genes are still lacking. In this work, we introduce SpaRTaCo, a new statistical model that clusters the spatial expression profiles of the genes according to a partition of the tissue. This is accomplished by performing a co-clustering, i.e., inferring the latent block structure of the data and inducing two types of clustering: of the genes, using their expression across the tissue, and of the image areas, using the gene expression in the spots where the RNA is collected. Our proposed methodology is validated with a series of simulation experiments and its usefulness in responding to specific biological questions is illustrated with an application to a human brain tissue sample processed with the 10X-Visium protocol.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 2","pages":"1444-1468"},"PeriodicalIF":1.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10552783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41163012","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

BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS. 电子病历中不平衡阳性未标记诊断码的贝叶斯分析。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-06-01 DOI: 10.1214/22-AOAS1666

Ru Wang, Ye Liang, Zhuqi Miao, Tieming Liu

{"title":"BAYESIAN ANALYSIS FOR IMBALANCED POSITIVE-UNLABELLED DIAGNOSIS CODES IN ELECTRONIC HEALTH RECORDS.","authors":"Ru Wang, Ye Liang, Zhuqi Miao, Tieming Liu","doi":"10.1214/22-AOAS1666","DOIUrl":"https://doi.org/10.1214/22-AOAS1666","url":null,"abstract":"With the increasing availability of electronic health records (EHR), significant progress has been made on developing predictive inference and algorithms by health data analysts and researchers. However, the EHR data are notoriously noisy due to missing and inaccurate inputs despite the information is abundant. One serious problem is that only a small portion of patients in the database has confirmatory diagnoses while many other patients remain undiagnosed because they did not comply with the recommended examinations. The phenomenon leads to a so-called positive-unlabelled situation and the labels are extremely imbalanced. In this paper, we propose a model-based approach to classify the unlabelled patients by using a Bayesian finite mixture model. We also discuss the label switching issue for the imbalanced data and propose a consensus Monte Carlo approach to address the imbalance issue and improve computational efficiency simultaneously. Simulation studies show that our proposed model-based approach outperforms existing positive-unlabelled learning algorithms. The proposed method is applied on the Cerner EHR for detecting diabetic retinopathy (DR) patients using laboratory measurements. With only 3% confirmatory diagnoses in the EHR database, we estimate the actual DR prevalence to be 25% which coincides with reported findings in the medical literature.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 2","pages":"1220-1238"},"PeriodicalIF":1.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10156089/pdf/nihms-1852796.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9563428","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

Robust joint modelling of left-censored longitudinal data and survival data with application to HIV vaccine studies. 左删失纵向数据和生存数据的稳健联合建模，并将其应用于艾滋病疫苗研究。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-06-01 Epub Date: 2023-05-01 DOI: 10.1214/22-aoas1656

Tingting Yu, Lang Wu, Jin Qiu, Peter B Gilbert

引用次数: 0

DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES. 利用存活树集合对中间事件引发的动态风险进行预测。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-06-01 Epub Date: 2023-05-01 DOI: 10.1214/22-aoas1674

Yifei Sun, Sy Han Chiou, Colin O Wu, Meghan McGarry, Chiung-Yu Huang

{"title":"DYNAMIC RISK PREDICTION TRIGGERED BY INTERMEDIATE EVENTS USING SURVIVAL TREE ENSEMBLES.","authors":"Yifei Sun, Sy Han Chiou, Colin O Wu, Meghan McGarry, Chiung-Yu Huang","doi":"10.1214/22-aoas1674","DOIUrl":"10.1214/22-aoas1674","url":null,"abstract":"With the availability of massive amounts of data from electronic health records and registry databases, incorporating time-varying patient information to improve risk prediction has attracted great attention. To exploit the growing amount of predictor information over time, we develop a unified framework for landmark prediction using survival tree ensembles, where an updated prediction can be performed when new information becomes available. Compared to conventional landmark prediction with fixed landmark times, our methods allow the landmark times to be subject-specific and triggered by an intermediate clinical event. Moreover, the nonparametric approach circumvents the thorny issue of model incompatibility at different landmark times. In our framework, both the longitudinal predictors and the event time outcome are subject to right censoring, and thus existing tree-based approaches cannot be directly applied. To tackle the analytical challenges, we propose a risk-set-based ensemble procedure by averaging martingale estimating equations from individual trees. Extensive simulation studies are conducted to evaluate the performance of our methods. The methods are applied to the Cystic Fibrosis Foundation Patient Registry (CFFPR) data to perform dynamic prediction of lung disease in cystic fibrosis patients and to identify important prognosis factors.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 2","pages":"1375-1397"},"PeriodicalIF":1.8,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10241448/pdf/nihms-1846847.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9974256","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

INDIVIDUALIZED RISK ASSESSMENT OF PREOPERATIVE OPIOID USE BY INTERPRETABLE NEURAL NETWORK REGRESSION. 可解释神经网络回归对术前阿片类药物使用的个体化风险评估。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-03-01 DOI: 10.1214/22-aoas1634

Yuming Sun, Jian Kang, Chad Brummett, Yi Li

{"title":"INDIVIDUALIZED RISK ASSESSMENT OF PREOPERATIVE OPIOID USE BY INTERPRETABLE NEURAL NETWORK REGRESSION.","authors":"Yuming Sun, Jian Kang, Chad Brummett, Yi Li","doi":"10.1214/22-aoas1634","DOIUrl":"https://doi.org/10.1214/22-aoas1634","url":null,"abstract":"Preoperative opioid use has been reported to be associated with higher preoperative opioid demand, worse postoperative outcomes, and increased postoperative healthcare utilization and expenditures. Understanding the risk of preoperative opioid use helps establish patient-centered pain management. In the field of machine learning, deep neural network (DNN) has emerged as a powerful means for risk assessment because of its superb prediction power; however, the blackbox algorithms may make the results less interpretable than statistical models. Bridging the gap between the statistical and machine learning fields, we propose a novel Interpretable Neural Network Regression (INNER), which combines the strengths of statistical and DNN models. We use the proposed INNER to conduct individualized risk assessment of preoperative opioid use. Intensive simulations and an analysis of 34,186 patients expecting surgery in the Analgesic Outcomes Study (AOS) show that the proposed INNER not only can accurately predict the preoperative opioid use using preoperative characteristics as DNN, but also can estimate the patient-specific odds of opioid use without pain and the odds ratio of opioid use for a unit increase in the reported overall body pain, leading to more straight-forward interpretations of the tendency to use opioids than DNN. Our results identify the patient characteristics that are strongly associated with opioid use and is largely consistent with the previous findings, providing evidence that INNER is a useful tool for individualized risk assessment of preoperative opioid use.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 1","pages":"434-453"},"PeriodicalIF":1.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10065608/pdf/nihms-1836641.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9282926","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

TOPOLOGICAL LEARNING FOR BRAIN NETWORKS. 脑网络拓扑学习

IF 1.3 4区数学

Annals of Applied Statistics Pub Date : 2023-03-01 Epub Date: 2023-01-24 DOI: 10.1214/22-aoas1633

Tananun Songdechakraiwut, Moo K Chung

引用次数: 0

MODELING CELL POPULATIONS MEASURED BY FLOW CYTOMETRY WITH COVARIATES USING SPARSE MIXTURE OF REGRESSIONS. 用稀疏混合回归的协变量对流式细胞术测量的细胞群进行建模。

IF 1.8 4区数学

Annals of Applied Statistics Pub Date : 2023-03-01 DOI: 10.1214/22-aoas1631

By Sangwon Hyun, Mattias Rolf Cape, Francois Ribalet, Jacob Bien

{"title":"MODELING CELL POPULATIONS MEASURED BY FLOW CYTOMETRY WITH COVARIATES USING SPARSE MIXTURE OF REGRESSIONS.","authors":"By Sangwon Hyun, Mattias Rolf Cape, Francois Ribalet, Jacob Bien","doi":"10.1214/22-aoas1631","DOIUrl":"https://doi.org/10.1214/22-aoas1631","url":null,"abstract":"The ocean is filled with microscopic microalgae, called phytoplankton, which together are responsible for as much photosynthesis as all plants on land combined. Our ability to predict their response to the warming ocean relies on understanding how the dynamics of phytoplankton populations is influenced by changes in environmental conditions. One powerful technique to study the dynamics of phytoplankton is flow cytometry which measures the optical properties of thousands of individual cells per second. Today, oceanographers are able to collect flow cytometry data in real time onboard a moving ship, providing them with fine-scale resolution of the distribution of phytoplankton across thousands of kilometers. One of the current challenges is to understand how these small- and large-scale variations relate to environmental conditions, such as nutrient availability, temperature, light and ocean currents. In this paper we propose a novel sparse mixture of multivariate regressions model to estimate the time-varying phytoplankton subpopulations while simultaneously identifying the specific environmental covariates that are predictive of the observed changes to these subpopulations. We demonstrate the usefulness and interpretability of the approach using both synthetic data and real observations collected on an oceanographic cruise conducted in the northeast Pacific in the spring of 2017.","PeriodicalId":50772,"journal":{"name":"Annals of Applied Statistics","volume":"17 1","pages":"357-377"},"PeriodicalIF":1.8,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10360992/pdf/nihms-1917146.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9905301","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: 4