Journal of the Royal Statistical Society Series C-Applied Statistics最新文献_第2页

IF 1.4 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

Measuring the impact of new risk factors within survival models. 在生存模型中测量新的风险因素的影响。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-09-03 eCollection Date: 2025-01-01 DOI: 10.1093/jrsssc/qlae045

Glenn Heller, Sean M Devlin

{"title":"Measuring the impact of new risk factors within survival models.","authors":"Glenn Heller, Sean M Devlin","doi":"10.1093/jrsssc/qlae045","DOIUrl":"10.1093/jrsssc/qlae045","url":null,"abstract":"Survival is poor for patients with metastatic cancer, and it is vital to examine new biomarkers that can improve patient prognostication and identify those who would benefit from more aggressive therapy. In metastatic prostate cancer, 2 new assays have become available: one that quantifies the number of cancer cells circulating in the peripheral blood, and the other a marker of the aggressiveness of the disease. It is critical to determine the magnitude of the effect of these biomarkers on the discrimination of a model-based risk score. To do so, most analysts frequently consider the discrimination of 2 separate survival models: one that includes both the new and standard factors and a second that includes the standard factors alone. However, this analysis is ultimately incorrect for many of the scale-transformation models ubiquitous in survival, as the reduced model is misspecified if the full model is specified correctly. To circumvent this issue, we developed a projection-based approach to estimate the impact of the 2 prostate cancer biomarkers. The results indicate that the new biomarkers can influence model discrimination and justify their inclusion in the risk model; however, the hunt remains for an applicable model to risk-stratify patients with metastatic prostate cancer.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"74 1","pages":"83-99"},"PeriodicalIF":1.3,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11725343/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142980653","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

Non-parametric Bayesian approach to multiple treatment comparisons in network meta-analysis with application to comparisons of anti-depressants. 网络荟萃分析中多重治疗比较的非参数贝叶斯方法，应用于抗抑郁药的比较。

IF 1.3 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2024-09-02 eCollection Date: 2024-11-01 DOI: 10.1093/jrsssc/qlae038

Andrés F Barrientos, Garritt L Page, Lifeng Lin

{"title":"Non-parametric Bayesian approach to multiple treatment comparisons in network meta-analysis with application to comparisons of anti-depressants.","authors":"Andrés F Barrientos, Garritt L Page, Lifeng Lin","doi":"10.1093/jrsssc/qlae038","DOIUrl":"10.1093/jrsssc/qlae038","url":null,"abstract":"Network meta-analysis is a powerful tool to synthesize evidence from independent studies and compare multiple treatments simultaneously. A critical task of performing a network meta-analysis is to offer ranks of all available treatment options for a specific disease outcome. Frequently, the estimated treatment rankings are accompanied by a large amount of uncertainty, suffer from multiplicity issues, and rarely permit possible ties of treatments with similar performance. These issues make interpreting rankings problematic as they are often treated as absolute metrics. To address these shortcomings, we formulate a ranking strategy that adapts to scenarios with high-order uncertainty by producing more conservative results. This improves the interpretability while simultaneously accounting for multiple comparisons. To admit ties between treatment effects in cases where differences between treatment effects are negligible, we also develop a Bayesian non-parametric approach for network meta-analysis. The approach capitalizes on the induced clustering mechanism of Bayesian non-parametric methods, producing a positive probability that two treatment effects are equal. We demonstrate the utility of the procedure through numerical experiments and a network meta-analysis designed to study antidepressant treatments.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 5","pages":"1333-1354"},"PeriodicalIF":1.3,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11561732/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142649544","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