journal
https://read.qxmd.com/read/38455841/a-bayesian-machine-learning-approach-for-estimating-heterogeneous-survivor-causal-effects-applications-to-a-critical-care-trial
#1
JOURNAL ARTICLE
Xinyuan Chen, Michael O Harhay, Guangyu Tong, Fan Li
Assessing heterogeneity in the effects of treatments has become increasingly popular in the field of causal inference and carries important implications for clinical decision-making. While extensive literature exists for studying treatment effect heterogeneity when outcomes are fully observed, there has been limited development in tools for estimating heterogeneous causal effects when patient-centered outcomes are truncated by a terminal event, such as death. Due to mortality occurring during study follow-up, the outcomes of interest are unobservable, undefined, or not fully observed for many participants in which case principal stratification is an appealing framework to draw valid causal conclusions...
March 2024: Annals of Applied Statistics
https://read.qxmd.com/read/38435672/anopow-for-replicated-nonstationary-time-series-in-experiments
#2
JOURNAL ARTICLE
Zeda Li, Yu Ryan Yue, Scott A Bruce
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...
March 2024: Annals of Applied Statistics
https://read.qxmd.com/read/38313601/using-simultaneous-regression-calibration-to-study-the-effect-of-multiple-error-prone-exposures-on-disease-risk-utilizing-biomarkers-developed-from-a-controlled-feeding-study
#3
JOURNAL ARTICLE
Yiwen Zhang, Ran Dai, Ying Huang, Ross Prentice, Cheng Zheng
Systematic measurement error in self-reported data creates important challenges in association studies between dietary intakes and chronic disease risks, especially when multiple dietary components are studied jointly. The joint regression calibration method has been developed for measurement error correction when objectively measured biomarkers are available for all dietary components of interest. Unfortunately, objectively measured biomarkers are only available for very few dietary components, which limits the application of the joint regression calibration method...
March 2024: Annals of Applied Statistics
https://read.qxmd.com/read/38283128/bayesian-hierarchical-modeling-and-analysis-for-actigraph-data-from-wearable-devices
#4
JOURNAL ARTICLE
Pierfrancesco Alaimo Di Loro, Marco Mingione, Jonah Lipsitt, Christina M Batteate, Michael Jerrett, Sudipto Banerjee
The majority of Americans fail to achieve recommended levels of physical activity, which leads to numerous preventable health problems such as diabetes, hypertension, and heart diseases. This has generated substantial interest in monitoring human activity to gear interventions toward environmental features that may relate to higher physical activity. Wearable devices, such as wrist-worn sensors that monitor gross motor activity (actigraph units) continuously record the activity levels of a subject, producing massive amounts of high-resolution measurements...
December 2023: Annals of Applied Statistics
https://read.qxmd.com/read/38250516/a-dynamic-additive-and-multiplicative-effects-network-model-with-application-to-the-united-nations-voting-behaviors
#5
JOURNAL ARTICLE
Bomin Kim, Xiaoyue Niu, David Hunter, Xun CaO
Motivated by a study of United Nations voting behaviors, we introduce a regression model for a series of networks that are correlated over time. Our model is a dynamic extension of the additive and multiplicative effects network model (AMEN) of Hoff (2021). In addition to incorporating a temporal structure, the model accommodates two types of missing data thus allows the size of the network to vary over time. We demonstrate via simulations the necessity of various components of the model. We apply the model to the United Nations General Assembly voting data from 1983 to 2014 (Voeten, 2013) to answer interesting research questions regarding international voting behaviors...
December 2023: Annals of Applied Statistics
https://read.qxmd.com/read/38149262/generalized-matrix-decomposition-regression-estimation-and-inference-for-two-way-structured-data
#6
JOURNAL ARTICLE
Yue Wang, Ali Shojaie, Timothy Randolph, Parker Knight, Jing Ma
Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR) to two-way structured data, but unlike PCR, GMDR selects the components that are most predictive of the outcome, leading to more accurate prediction...
December 2023: Annals of Applied Statistics
https://read.qxmd.com/read/38106966/debiased-lasso-for-stratified-cox-models-with-application-to-the-national-kidney-transplant-data
#7
JOURNAL ARTICLE
Lu Xia, Bin Nan, Yi Li
The Scientific Registry of Transplant Recipients (SRTR) system has become a rich resource for understanding the complex mechanisms of graft failure after kidney transplant, a crucial step for allocating organs effectively and implementing appropriate care. As transplant centers that treated patients might strongly confound graft failures, Cox models stratified by centers can eliminate their confounding effects. Also, since recipient age is a proven non-modifiable risk factor, a common practice is to fit models separately by recipient age groups...
December 2023: Annals of Applied Statistics
https://read.qxmd.com/read/38046186/pairwise-nonlinear-dependence-analysis-of-genomic-data
#8
JOURNAL ARTICLE
Siqi Xiang, Wan Zhang, Siyao Liu, Katherine A Hoadley, Charles M Perou, Kai Zhang, J S Marron
In The Cancer Genome Atlas (TCGA) data set, there are many interesting nonlinear dependencies between pairs of genes that reveal important relationships and subtypes of cancer. Such genomic data analysis requires a rapid, powerful and interpretable detection process, especially in a high-dimensional environment. We study the nonlinear patterns among the expression of pairs of genes from TCGA using a powerful tool called Binary Expansion Testing. We find many nonlinear patterns, some of which are driven by known cancer subtypes, some of which are novel...
December 2023: Annals of Applied Statistics
https://read.qxmd.com/read/38250709/estimating-causal-effects-of-hiv-prevention-interventions-with-interference-in-network-based-studies-among-people-who-inject-drugs
#9
JOURNAL ARTICLE
TingFang Lee, Ashley L Buchanan, Natallia V Katenka, Laura Forastiere, M Elizabeth Halloran, Samuel R Friedman, Georgios Nikolopoulos
Evaluating causal effects in the presence of interference is challenging in network-based studies of hard-to-reach populations. Like many such populations, people who inject drugs (PWID) are embedded in social networks and often exert influence on others in their network. In our setting, the study design is observational with a non-randomized network-based HIV prevention intervention. Information is available on each participant and their connections that confer possible HIV risk through injection and sexual behaviors...
September 2023: Annals of Applied Statistics
https://read.qxmd.com/read/38037614/dynamic-prediction-of-residual-life-with-longitudinal-covariates-using-long-short-term-memory-networks
#10
JOURNAL ARTICLE
Grace Rhodes, Marie Davidian, Wenbin Lu
Sepsis, a complex medical condition that involves severe infections with life-threatening organ dysfunction, is a leading cause of death worldwide. Treatment of sepsis is highly challenging. When making treatment decisions, clinicians and patients desire accurate predictions of mean residual life (MRL) that leverage all available patient information, including longitudinal biomarker data. Biomarkers are biological, clinical, and other variables reflecting disease progression that are often measured repeatedly on patients in the clinical setting...
September 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37830084/the-scalable-birth-death-mcmc-algorithm-for-mixed-graphical-model-learning-with-application-to-genomic-data-integration
#11
JOURNAL ARTICLE
Nanwei Wang, Hélène Massam, Xin Gao, Laurent Briollais
Recent advances in biological research have seen the emergence of high-throughput technologies with numerous applications that allow the study of biological mechanisms at an unprecedented depth and scale. A large amount of genomic data is now distributed through consortia like The Cancer Genome Atlas (TCGA), where specific types of biological information on specific type of tissue or cell are available. In cancer research, the challenge is now to perform integrative analyses of high-dimensional multi-omic data with the goal to better understand genomic processes that correlate with cancer outcomes, e...
September 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37786772/bayesian-combinatorial-multistudy-factor-analysis
#12
JOURNAL ARTICLE
Isabella N Grabski, Roberta De Vito, Lorenzo Trippa, Giovanni Parmigiani
Mutations in the BRCA1 and BRCA2 genes are known to be highly associated with breast cancer. Identifying both shared and unique transcript expression patterns in blood samples from these groups can shed insight into if and how the disease mechanisms differ among individuals by mutation status, but this is challenging in the high-dimensional setting. A recent method, Bayesian Multi-Study Factor Analysis (BMSFA), identifies latent factors common to all studies (or equivalently, groups) and latent factors specific to individual studies...
September 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37719893/bayesian-inference-and-dynamic-prediction-for-multivariate-longitudinal-and-survival-data
#13
JOURNAL ARTICLE
Haotian Zou, Donglin Zeng, Luo Xiao, Sheng Luo
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...
September 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37711665/probabilistic-learning-of-treatment-trees-in-cancer
#14
JOURNAL ARTICLE
Tsung-Hung Yao, Zhenke Wu, Karthik Bharath, Jinju Li, Veerabhadran Baladandayuthapani
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)...
September 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37811520/co-clustering-of-spatially-resolved-transcriptomic-data
#15
JOURNAL ARTICLE
Andrea Sottosanti, Davide Risso
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...
June 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37396148/robust-joint-modelling-of-left-censored-longitudinal-data-and-survival-data-with-application-to-hiv-vaccine-studies
#16
JOURNAL ARTICLE
Tingting Yu, Lang Wu, Jin Qiu, Peter B Gilbert
In jointly modelling longitudinal and survival data, the longitudinal data may be complex in the sense that they may contain outliers and may be left censored. Motivated from an HIV vaccine study, we propose a robust method for joint models of longitudinal and survival data, where the outliers in longitudinal data are addressed using a multivariate t-distribution for b-outliers and using an M-estimator for e-outliers. We also propose a computationally efficient method for approximate likelihood inference. The proposed method is evaluated by simulation studies...
June 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37396147/identification-of-immune-response-combinations-associated-with-heterogeneous-infection-risk-in-the-immune-correlates-analysis-of-hiv-vaccine-studies
#17
JOURNAL ARTICLE
Chaeryon Kang, Ying Huang
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...
June 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37284167/dynamic-risk-prediction-triggered-by-intermediate-events-using-survival-tree-ensembles
#18
JOURNAL ARTICLE
Yifei Sun, Sy Han Chiou, Colin O Wu, Meghan McGarry, Chiung-Yu Huang
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...
June 2023: Annals of Applied Statistics
https://read.qxmd.com/read/37152904/bayesian-analysis-for-imbalanced-positive-unlabelled-diagnosis-codes-in-electronic-health-records
#19
JOURNAL ARTICLE
Ru Wang, Ye Liang, Zhuqi Miao, Tieming Liu
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...
June 2023: Annals of Applied Statistics
https://read.qxmd.com/read/38486612/bayesian-non-homogeneous-hidden-markov-model-with-variable-selection-for-investigating-drivers-of-seizure-risk-cycling
#20
JOURNAL ARTICLE
Emily T Wang, Sharon Chiang, Zulfi Haneef, Vikram R Rao, Robert Moss, Marina Vannucci
A major issue in the clinical management of epilepsy is the unpredictability of seizures. Yet, traditional approaches to seizure forecasting and risk assessment in epilepsy rely heavily on raw seizure frequencies, which are a stochastic measurement of seizure risk. We consider a Bayesian non-homogeneous hidden Markov model for unsupervised clustering of zero-inflated seizure count data. The proposed model allows for a probabilistic estimate of the sequence of seizure risk states at the individual level. It also offers significant improvement over prior approaches by incorporating a variable selection prior for the identification of clinical covariates that drive seizure risk changes and accommodating highly granular data...
March 2023: Annals of Applied Statistics
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