Journal of the Royal Statistical Society Series C-Applied Statistics最新文献

Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approach. 暴露缓冲和风险的空间异质性建模：层次贝叶斯方法。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2026-03-31 DOI: 10.1093/jrsssc/qlag020

Saskia Comess, Daniel E Ho, Joshua L Warren

{"title":"Modelling spatial heterogeneity in exposure buffers and risk: a hierarchical Bayesian approach.","authors":"Saskia Comess, Daniel E Ho, Joshua L Warren","doi":"10.1093/jrsssc/qlag020","DOIUrl":"https://doi.org/10.1093/jrsssc/qlag020","url":null,"abstract":"Place-based epidemiology studies often rely on circular buffers to define 'exposure' to spatially distributed risk factors, where the buffer radius represents a threshold beyond which exposure does not influence the outcome of interest. This approach is popular due to its simplicity and alignment with public health policies. However, buffer radii are often chosen relatively arbitrarily and assumed constant across the spatial domain. This may result in suboptimal statistical inference if these modelling choices are incorrect. To address this, we develop spatially varying buffer radii (SVBR), a flexible hierarchical Bayesian spatial change points approach that treats buffer radii as unknown parameters and allows both radii and exposure effects to vary spatially. Through simulations, we find that SVBR improves estimation and inference for key model parameters compared to traditional methods. We also apply SVBR to study healthcare access in Madagascar, finding that proximity to healthcare facilities generally increases antenatal care usage, with clear spatial variation in this relationship. By relaxing rigid assumptions about buffer characteristics, our method offers a flexible, data-driven approach to accurately defining exposure and quantifying its impact. The newly developed methods are available in the R package EpiBuffer.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13142215/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147845431","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

Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration. 精准心理健康：通过数据整合预测抑郁症的异质性治疗效果。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-12-12 DOI: 10.1093/jrsssc/qlaf068

Carly L Brantner, Trang Quynh Nguyen, Harsh Parikh, Congwen Zhao, Hwanhee Hong, Elizabeth A Stuart

{"title":"Precision Mental Health: Predicting Heterogeneous Treatment Effects for Depression through Data Integration.","authors":"Carly L Brantner, Trang Quynh Nguyen, Harsh Parikh, Congwen Zhao, Hwanhee Hong, Elizabeth A Stuart","doi":"10.1093/jrsssc/qlaf068","DOIUrl":"10.1093/jrsssc/qlaf068","url":null,"abstract":"When treating depression, clinicians are interested in determining the optimal treatment for a given patient, which is challenging given the amount of treatments available. To advance individualized treatment allocation, integrating data across multiple randomized controlled trials (RCTs) can enhance our understanding of treatment effect heterogeneity by increasing available information. However, extending these inferences to individuals outside of the original RCTs remains crucial for clinical decision-making. We introduce a two-stage meta-analytic method that predicts conditional average treatment effects (CATEs) in target patient populations by leveraging the distribution of CATEs across RCTs. Our approach generates 95% prediction intervals for CATEs in target settings using first-stage models that can incorporate parametric regression or non-parametric methods such as causal forests or Bayesian additive regression trees (BART). We validate our method through simulation studies and operationalize it to integrate multiple RCTs comparing depression treatments, duloxetine and vortioxetine, to generate prediction intervals for target patient profiles. Our analysis reveals no strong evidence of effect heterogeneity across trials, with the exception of potential age-related variability. Importantly, we show that CATE prediction intervals capture broader uncertainty than study-specific confidence intervals when warranted, reflecting both within-study and between-study variability.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12768387/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145913599","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

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-06-01 Epub Date: 2025-01-23 DOI: 10.1093/jrsssc/qlaf001

Duo Yu, Meilin Huang, Michael J Kane, Brian P Hobbs

引用次数: 0

Robust sparse Bayesian regression for longitudinal gene-environment interactions. 纵向基因-环境相互作用的鲁棒稀疏贝叶斯回归。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-04-08 eCollection Date: 2025-12-01 DOI: 10.1093/jrsssc/qlaf027

Kun Fan, Yu Jiang, Shuangge Ma, Weiqun Wang, Cen Wu

{"title":"Robust sparse Bayesian regression for longitudinal gene-environment interactions.","authors":"Kun Fan, Yu Jiang, Shuangge Ma, Weiqun Wang, Cen Wu","doi":"10.1093/jrsssc/qlaf027","DOIUrl":"10.1093/jrsssc/qlaf027","url":null,"abstract":"In longitudinal studies, repeated measure analysis of variance (ANOVA) is a classical analysis where selecting important main and interaction effects for accurate estimation and prediction is among one of its central goals. With high-dimensional genetic factors, ANOVA leads to a sparse longitudinal gene-environment ( <math><mrow><mi>G</mi></mrow> <mo>×</mo> <mrow><mi>E</mi></mrow> </math> ) interaction problem that has not been thoroughly investigated so far, partially due to the challenges to incorporate robustness against skewed phenotypic measurements, intra-cluster correlations among longitudinal observations, and structured sparsity arising from the ANOVA design. We have developed a novel robust sparse Bayesian mixed model to tackle these challenges. Outliers and inter-relatedness among repeated measurements can be efficiently accommodated. Meanwhile, the proposed model conducts robust Bayesian variable selection accounting for main and interaction effects via structured spike-and-slab priors. We have developed Gibbs samplers and MCMC algorithms for fast computation and posterior inference. The advantage of the proposed method over benchmarks in variable selection and estimation has been established through extensive simulations. In the case study, we have analysed longitudinal lipidomics data with repeatedly measured body weight of CD-1 mice from a cancer prevention study. The proposed model has identified main and interactions with important implications and led to better prediction performance over alternative methods.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"74 5","pages":"1372-1394"},"PeriodicalIF":1.3,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12617437/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145542853","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

Correcting for bias due to mismeasured exposure in mediation analysis with a survival outcome. 校正因存在生存结果的中介分析中暴露量测量错误造成的偏倚。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-02-14 eCollection Date: 2025-11-01 DOI: 10.1093/jrsssc/qlaf010

Chao Cheng, Donna Spiegelman, Fan Li

引用次数: 0

Synergistic self-learning approach to establishing individualized treatment rules from multiple benefit outcomes in a calcium supplementation trial. 从补钙试验的多重获益结果建立个体化治疗规则的协同自学习方法。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-02-14 eCollection Date: 2025-11-01 DOI: 10.1093/jrsssc/qlaf008

Yiwang Zhou, Peter X K Song

引用次数: 0

Building absolute breast cancer risk prediction models for women treated with chest radiation for Hodgkin lymphoma. 建立霍奇金淋巴瘤胸部放射治疗女性乳腺癌绝对风险预测模型。

IF 1 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-01-03 eCollection Date: 2025-03-01 DOI: 10.1093/jrsssc/qlae063

Sander Roberti, Flora E van Leeuwen, Michael Hauptmann, Ruth M Pfeiffer

引用次数: 0

Inferring bivariate associations with continuous data from studies using respondent-driven sampling. 利用调查对象驱动的抽样研究的连续数据推断双变量关联。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-11-26 eCollection Date: 2025-03-01 DOI: 10.1093/jrsssc/qlae061

Samantha Malatesta, Karen R Jacobson, Tara Carney, Eric D Kolaczyk, Krista J Gile, Laura F White

{"title":"Inferring bivariate associations with continuous data from studies using respondent-driven sampling.","authors":"Samantha Malatesta, Karen R Jacobson, Tara Carney, Eric D Kolaczyk, Krista J Gile, Laura F White","doi":"10.1093/jrsssc/qlae061","DOIUrl":"10.1093/jrsssc/qlae061","url":null,"abstract":"Respondent-driven sampling (RDS) is a link-tracing sampling design that was developed to sample from hidden populations. Although associations between variables are of great interest in epidemiological research, there has been little statistical work on inference on relationships between variables collected through RDS. The link-tracing design, combined with homophily, the tendency for people to connect to others with whom they share characteristics, induces similarity between linked individuals. This dependence inflates the Type 1 error of conventional statistical methods (e.g. t-tests, regression, etc.). A semiparametric randomization test for bivariate association was developed to test for association between two categorical variables. We directly extend this work and propose a semiparametric randomization test for relationships between two variables, when one or both are continuous. We apply our method to variables that are important for understanding tuberculosis epidemiology among people who smoke illicit drugs in Worcester, South Africa.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"74 2","pages":"429-446"},"PeriodicalIF":1.3,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905883/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651715","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

Multivariate Bayesian variable selection for multi-trait genetic fine mapping. 多性状遗传精细定位的多元贝叶斯变量选择。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-10-28 eCollection Date: 2025-03-01 DOI: 10.1093/jrsssc/qlae055

Travis Canida, Hongjie Ke, Shuo Chen, Zhenyao Ye, Tianzhou Ma

{"title":"Multivariate Bayesian variable selection for multi-trait genetic fine mapping.","authors":"Travis Canida, Hongjie Ke, Shuo Chen, Zhenyao Ye, Tianzhou Ma","doi":"10.1093/jrsssc/qlae055","DOIUrl":"10.1093/jrsssc/qlae055","url":null,"abstract":"Genome-wide association studies (GWAS) have identified thousands of single-nucleotide polymorphisms (SNPs) associated with complex traits, but determining the underlying causal variants remains challenging. Fine mapping aims to pinpoint the potentially causal variants from a large number of correlated SNPs possibly with group structure in GWAS-enriched genomic regions using variable selection approaches. In multi-trait fine mapping, we are interested in identifying the causal variants for multiple related traits. Existing multivariate variable selection methods for fine mapping select variables for all responses without considering the possible heterogeneity across different responses. Here, we develop a novel multivariate Bayesian variable selection method for multi-trait fine mapping to select causal variants from a large number of grouped SNPs that target at multiple correlated and possibly heterogeneous traits. Our new method is featured by its selection at multiple levels, incorporation of prior biological knowledge to guide selection and identification of best subset of traits the variants target at. We showed the advantage of our method over existing methods via comprehensive simulations that mimic typical fine-mapping settings and a real-world fine-mapping example in UK Biobank, where we identified critical causal variants potentially targeting at different subsets of addictive behaviours and risk factors.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"74 2","pages":"331-351"},"PeriodicalIF":1.3,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11905884/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143651720","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

tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction. tdCoxSNN：用于连续时间动态预测的时变Cox生存神经网络。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-10-11 eCollection Date: 2025-01-01 DOI: 10.1093/jrsssc/qlae051

Lang Zeng, Jipeng Zhang, Wei Chen, Ying Ding

{"title":"tdCoxSNN: Time-dependent Cox survival neural network for continuous-time dynamic prediction.","authors":"Lang Zeng, Jipeng Zhang, Wei Chen, Ying Ding","doi":"10.1093/jrsssc/qlae051","DOIUrl":"10.1093/jrsssc/qlae051","url":null,"abstract":"The aim of dynamic prediction is to provide individualized risk predictions over time, which are updated as new data become available. In pursuit of constructing a dynamic prediction model for a progressive eye disorder, age-related macular degeneration (AMD), we propose a time-dependent Cox survival neural network (tdCoxSNN) to predict its progression using longitudinal fundus images. tdCoxSNN builds upon the time-dependent Cox model by utilizing a neural network to capture the nonlinear effect of time-dependent covariates on the survival outcome. Moreover, by concurrently integrating a convolutional neural network with the survival network, tdCoxSNN can directly take longitudinal images as input. We evaluate and compare our proposed method with joint modelling and landmarking approaches through extensive simulations. We applied the proposed approach to two real datasets. One is a large AMD study, the Age-Related Eye Disease Study, in which more than 50,000 fundus images were captured over a period of 12 years for more than 4,000 participants. Another is a public dataset of the primary biliary cirrhosis disease, where multiple laboratory tests were longitudinally collected to predict the time-to-liver transplant. Our approach demonstrates commendable predictive performance in both simulation studies and the analysis of the two real datasets.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"74 1","pages":"187-203"},"PeriodicalIF":1.3,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11725344/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980658","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