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

Refined trial sequential analysis for meta-analyses with few studies. 精细化的试验序列分析，用于较少研究的荟萃分析。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2026-08-28 DOI: 10.1093/jrsssc/qlag050

Yipeng Wang, Lifeng Lin

{"title":"Refined trial sequential analysis for meta-analyses with few studies.","authors":"Yipeng Wang, Lifeng Lin","doi":"10.1093/jrsssc/qlag050","DOIUrl":"10.1093/jrsssc/qlag050","url":null,"abstract":"Evidence syntheses are often updated as new trials become available. A cumulative meta-analysis repeats a meta-analysis in chronological order, and trial sequential analysis applies group-sequential principles to cumulative meta-analysis to control the risk of spurious findings due to repeated testing. Despite the growing popularity of trial sequential analysis, existing methods rely heavily on normal approximations derived from interim analyses of randomized controlled trials, where participants are typically more homogeneous than in meta-analyses. In random-effects meta-analyses, the conventional assumption that the synthesized effect estimate follows a normal distribution can perform poorly when the number of studies is small. In such settings, the Hartung-Knapp-Sidik-Jonkman method, which is based on a <math><mi>t</mi></math> distribution, has been recommended for more reliable inference. This article introduces refined trial sequential procedures based on cumulative meta-analytic <math><mi>t</mi></math> statistics. The proposed methods are designed to reduce the risk of premature or spurious conclusions in updating evidence syntheses, particularly when between-study heterogeneity is present. Numerical studies demonstrate that the proposed methods provide improved control of type I error compared with existing methods, although the degree of improvement depends on the magnitude of heterogeneity and the true effect size.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13524431/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148851909","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

Multi-Tree Model for Precision Environmental Health with Longitudinally Assessed Mixture Exposure. 具有纵向评估混合暴露的精确环境健康多树模型。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2026-08-18 DOI: 10.1093/jrsssc/qlag047

Seongwon Im, Daniel Mork, Ander Wilson

{"title":"Multi-Tree Model for Precision Environmental Health with Longitudinally Assessed Mixture Exposure.","authors":"Seongwon Im, Daniel Mork, Ander Wilson","doi":"10.1093/jrsssc/qlag047","DOIUrl":"10.1093/jrsssc/qlag047","url":null,"abstract":"Precision environmental health seeks to estimate how the effects of the environment vary across the population to inform targeted interventions and public health policy. However, there is a lack of statistical methods to estimate the heterogeneous effects of environmental exposures, particularly mixture exposures that are assessed longitudinally. We examine the heterogeneous exposure effect of weekly-average fine particulate matter (PM2.5) and maximal daily temperature during gestation on birth weight using birth registry data in Colorado. We develop a Bayesian additive model represented by an ensemble of tree triplets where a tree triplet consists of two types of binary trees, interacting to model heterogeneous time-structured exposure effects. Our framework provides a tool to estimate individualized and subgroup-specific distributed lag effects of longitudinally assessed mixture exposures. Our method can accommodate a high-dimensional set of candidate modifiers with modifier selection and allows for mixture exposures with time-sensitive interactions. Through simulation, we demonstrate that our model can estimate individualized exposure effects and identify important mixture components and modifying factors. From the Colorado birth registry data, we find evidence of an association between PM2.5 and birth weight with larger effects among mothers younger than 25 years with low income.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13580615/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148992308","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 Cure from Cancer Accounting for Inevitable Mortality. 对癌症治疗的建模，以解释不可避免的死亡率。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2026-07-21 DOI: 10.1093/jrsssc/qlag040

Suvra Pal, Suchitrita Sarkar Rathmann, Qi Jiang, Jiehuan Sun, Debajyoti Sinha, Sanjib Basu

{"title":"Modeling Cure from Cancer Accounting for Inevitable Mortality.","authors":"Suvra Pal, Suchitrita Sarkar Rathmann, Qi Jiang, Jiehuan Sun, Debajyoti Sinha, Sanjib Basu","doi":"10.1093/jrsssc/qlag040","DOIUrl":"10.1093/jrsssc/qlag040","url":null,"abstract":"Cancer remains the second most prevalent cause of death in the United States, claiming 605,213 lives in 2021, surpassing COVID-19 deaths. The cancer mortality rate continued to decline between 2019 and 2020, dropping by 1.5%, marking a significant 33% decrease since 1991. This ongoing improvement primarily mirrors advances in treatment, allowing patients to achieve clinical remission and recovery. Now, a cancer patient is simultaneously exposed to the risk of primary cancer as well as other risks, such as other cancer(s) or other diseases, leading to a competing risks scenario. Analysis of survival data under competing risks and the presence of cured patients have been extensively studied individually, but there is limited work in the current literature that models the possibility of cure from one risk in the presence of competing risks. Moreover, such a model should allow for the possibility of cure from the cause-specific risk of the primary cancer; however, the overall survival probability should eventually approach zero, thereby incorporating the prevalent belief of eventual failure with certainty. We propose a novel unified competing risks cure model, based on the cause-specific hazard approach, that satisfies the aforementioned desired properties. The conditions required to establish model identifiability are studied in detail. To find the maximum likelihood estimates of the model parameters, a computationally efficient expectation maximization algorithm is developed. An extensive simulation study is carried out to demonstrate the performance of the proposed model and estimation method under different parameter settings and in the presence of multiple competing risks. Finally, an application is illustrated using breast cancer data from the SEER cancer database.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13426226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148654859","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

Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models. 通过跨维马尔可夫链蒙特卡罗和隐马尔可夫模型揭示癌症表观遗传学的变化。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2026-06-01 Epub Date: 2025-11-06 DOI: 10.1093/jrsssc/qlaf059

Farhad Shokoohi, Saeedeh Hajebi Khaniki

{"title":"Uncovering alterations in cancer epigenetics via trans-dimensional Markov chain Monte Carlo and hidden Markov models.","authors":"Farhad Shokoohi, Saeedeh Hajebi Khaniki","doi":"10.1093/jrsssc/qlaf059","DOIUrl":"10.1093/jrsssc/qlaf059","url":null,"abstract":"Epigenetic alterations are key drivers in the development and progression of cancer. Identifying differentially methylated cytosines (DMCs) in cancer samples is a crucial step toward understanding these changes. In this paper, we propose a trans-dimensional Markov chain Monte Carlo (TMCMC) approach that uses hidden Markov models (HMMs) with binomial emission, and bisulfite sequencing (BS-Seq) data, called DMCTHM, to identify DMCs in cancer epigenetic studies. We introduce the Expander-Collider penalty to tackle under and over-estimation in TMCMC-HMMs. We address all known challenges inherent in BS-Seq data by introducing novel approaches for capturing functional patterns and autocorrelation structure of the data, as well as for handling missing values, multiple covariates, multiple comparisons, and family-wise errors. We demonstrate the effectiveness of DMCTHM through comprehensive simulation studies. The results show that our proposed method outperforms other competing methods in identifying DMCs. Notably, with DMCTHM, we uncovered new DMCs and genes in Colorectal cancer that were significantly enriched in the Tp53 pathway.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"75 3","pages":"601-623"},"PeriodicalIF":1.4,"publicationDate":"2026-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13322287/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148370605","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

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 1.4 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":"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":1.4,"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

Robust domain selection for functional data via interval-wise testing and effect size mapping. 通过区间测试和效应大小映射对功能数据进行鲁棒域选择。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2026-03-05 DOI: 10.1093/jrsssc/qlag014

Yeonjoo Park, Aiguo Han

{"title":"Robust domain selection for functional data via interval-wise testing and effect size mapping.","authors":"Yeonjoo Park, Aiguo Han","doi":"10.1093/jrsssc/qlag014","DOIUrl":"10.1093/jrsssc/qlag014","url":null,"abstract":"Among inferential problems in functional data analysis, domain selection is one of the practical interests aiming to identify sub-interval(s) of the domain where desired functional features are displayed. Motivated by applications in quantitative ultrasound signal analysis, we propose the robust domain selection method, particularly aiming to discover a subset of the domain presenting distinct behaviors on location parameters among different groups. By extending the interval testing approach, we propose to take into account multiple aspects of functional features simultaneously to detect the practically interpretable domain. To further handle potential outliers and missing segments on collected functional trajectories, we perform interval testing with a test statistic based on functional M-estimators for the inference. In addition, we introduce the effect size heatmap by calculating robustified effect sizes from the lowest to the largest scales over the domain to reflect dynamic functional behaviors among groups so that clinicians get a comprehensive understanding and select practically meaningful sub-interval(s). The performance of the proposed method is demonstrated through simulation studies and an application to motivating quantitative ultrasound measurements.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":" ","pages":""},"PeriodicalIF":1.4,"publicationDate":"2026-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266663","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

Adaptive Fisher's method using weakly geometric grid for combining p-values with application to COVID-19 surveillance. 基于弱几何网格的p值组合自适应Fisher方法在COVID-19监测中的应用。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-12-12 eCollection Date: 2026-06-01 DOI: 10.1093/jrsssc/qlaf069

Yusi Fang, Zhao Ren, George C Tseng

{"title":"Adaptive Fisher's method using weakly geometric grid for combining p-values with application to COVID-19 surveillance.","authors":"Yusi Fang, Zhao Ren, George C Tseng","doi":"10.1093/jrsssc/qlaf069","DOIUrl":"10.1093/jrsssc/qlaf069","url":null,"abstract":"In COVID-19 surveillance, detecting significant case increases within regions over specific periods is crucial. Classical methods, typically relying on strict parametric assumptions, struggle with the rare events characteristic of COVID-19's early spread. An alternative strategy is employing nonparametric approaches based on p-value combination methods. However, initial COVID-19 outbreaks across regions exhibit varying signal sparsity levels, while existing p-value combination methods demonstrate power in detecting either moderately sparse or ultra sparse signals in practice, but not both. We present a modified Fisher's method, utilizing a weakly geometric system-based search strategy to adapt across the entire spectrum of signal sparsity. Our method is theoretically and numerically powerful across the whole spectrum of sparsity. Under mild conditions, we examine the robustness of our method in combining approximated p-values, demonstrating its powerful performance even when the number of p-values far surpasses the sample sizes for their derivation, offering a novel nonparametric strategy for COVID-19 surveillance. An efficient algorithm is developed to calculate the p-value of our method. Focusing on the early COVID-19 surveillance in the United States, our method consistently detects outbreaks across regions with varying signal sparsity, uncovering diverse patterns of COVID-19's spread, while competing methods struggle with either ultra-sparse or moderately sparse signals.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"75 3","pages":"699-717"},"PeriodicalIF":1.4,"publicationDate":"2025-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268682/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148273603","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 1.4 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":1.4,"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

Doubly regularized generalized linear models for spatial observations with high-dimensional covariates. 高维协变量空间观测的双正则化广义线性模型。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-12-01 Epub Date: 2025-03-25 DOI: 10.1093/jrsssc/qlaf023

Arjun Sondhi, Si Cheng, Ali Shojaie

{"title":"Doubly regularized generalized linear models for spatial observations with high-dimensional covariates.","authors":"Arjun Sondhi, Si Cheng, Ali Shojaie","doi":"10.1093/jrsssc/qlaf023","DOIUrl":"10.1093/jrsssc/qlaf023","url":null,"abstract":"A discrete spatial lattice can be cast as a network structure over which spatially-correlated outcomes are observed. A second network structure may also capture similarities among measured features, when such information is available. Incorporating the network structures when analyzing such doubly-structured data can improve predictive power, and lead to better identification of important features in the data-generating process. Motivated by applications in spatial disease mapping, we develop a new doubly regularized regression framework to incorporate these network structures for analyzing high-dimensional datasets. Our estimators can be easily implemented with standard convex optimization algorithms. In addition, we describe a procedure to obtain asymptotically valid confidence intervals and hypothesis tests for our model parameters. We show empirically that our framework provides improved predictive accuracy and inferential power compared to existing high-dimensional spatial methods. These advantages hold given fully accurate network information, and also with networks which are partially misspecified or uninformative. The application of the proposed method to modeling COVID-19 mortality data suggests that it can improve the prediction of deaths beyond standard spatial models, and that it selects relevant covariates more often.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"74 5","pages":"1279-1300"},"PeriodicalIF":1.4,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13298378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148347156","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

Statistical methods for cost-effectiveness analysis of left-truncated and right-censored survival data with treatment delays. 治疗延迟的左截和右截生存数据成本-效果分析的统计方法。

IF 1.4 4区数学

Journal of the Royal Statistical Society Series C-Applied Statistics Pub Date : 2025-06-17 eCollection Date: 2025-12-01 DOI: 10.1093/jrsssc/qlaf035

Polyna Khudyakov, Li Xu, Ce Yang, Donna Spiegelman, Molin Wang

引用次数: 0