Statistics in Medicine最新文献_第6页

RISE: Two-Stage Rank-Based Identification of High-Dimensional Surrogate Markers Applied to Vaccinology. RISE：用于疫苗学的高维替代标记的两阶段基于等级的鉴定。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70241

Arthur Hughes, Layla Parast, Rodolphe Thiébaut, Boris P Hejblum

{"title":"RISE: Two-Stage Rank-Based Identification of High-Dimensional Surrogate Markers Applied to Vaccinology.","authors":"Arthur Hughes, Layla Parast, Rodolphe Thiébaut, Boris P Hejblum","doi":"10.1002/sim.70241","DOIUrl":"10.1002/sim.70241","url":null,"abstract":"In vaccine trials with long-term participant follow-up, it is of great importance to identify surrogate markers that accurately infer long-term immune responses. These markers offer practical advantages such as providing early, indirect evidence of vaccine efficacy, and can accelerate vaccine development while identifying potential biomarkers. High-throughput technologies such as RNA-sequencing have emerged as promising tools for understanding complex biological systems and informing new treatment strategies. However, these data are high-dimensional, presenting unique statistical challenges for existing surrogate marker identification methods. We introduce Rank-based Identification of high-dimensional SurrogatE Markers (RISE), a novel approach designed for small sample, high-dimensional settings typical in modern vaccine experiments. RISE uses a nonparametric univariate test to screen variables for promising candidates, followed by surrogate evaluation on independent data. Our simulation studies demonstrate RISE's desirable properties, including type one error rate control and empirical power under various conditions. Applying RISE to a clinical trial for inactivated influenza vaccination, we sought to identify genes whose expression could serve as a surrogate for the induced immune response. This analysis revealed a signature of genes appearing to function as a reasonable surrogate for the neutralizing antibody response. Pathways related to innate antiviral signaling and interferon stimulation were strongly represented in this derived surrogate, providing a clear immunological interpretation.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70241"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12412727/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Grouped Horseshoe Priors for Subgroup Identification and Estimation. 基于分层分组马蹄先验的子群识别与估计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70246

Ethan M Alt, Anil Anderson, Qing Li, Jia Hua, Amarjot Kaur, Joseph G Ibrahim

引用次数: 0

Modeling Joint Health Effects of Environmental Exposure Mixtures With Bayesian Additive Regression Trees. 用贝叶斯加性回归树模拟环境暴露混合物对关节健康的影响。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70250

Jacob R Englert, Stefanie T Ebelt, Howard H Chang

{"title":"Modeling Joint Health Effects of Environmental Exposure Mixtures With Bayesian Additive Regression Trees.","authors":"Jacob R Englert, Stefanie T Ebelt, Howard H Chang","doi":"10.1002/sim.70250","DOIUrl":"10.1002/sim.70250","url":null,"abstract":"Studying the association between mixtures of environmental exposures and health outcomes can be challenging due to issues such as correlation among the exposures and non-linearities or interactions in the exposure-response function. For this reason, one common strategy is to fit flexible nonparametric models to capture the true exposure-response surface. However, once such a model is fit, further decisions are required when it comes to summarizing the marginal and joint effects of the mixture on the outcome. In this work, we describe the use of soft Bayesian additive regression trees (BART) to estimate the exposure-risk surface describing the effect of mixtures of chemical air pollutants and temperature on asthma-related emergency department (ED) visits during the warm season in Atlanta, Georgia, from 2011 to 2018. BART is chosen for its ability to handle large datasets and for its flexibility to be incorporated as a single component of a larger model. We then summarize the results using a strategy known as accumulated local effects to extract meaningful insights into the mixture effects on asthma-related morbidity. Notably, we observe negative associations between <math> <semantics> <mrow> <msub><mrow><mtext>NO</mtext></mrow> <mrow><mn>2</mn></mrow> </msub> </mrow> <annotation>$$ {mathrm{NO}}_2 $$</annotation></semantics> </math> and asthma ED visits and harmful associations between ozone and asthma ED visits, both of which are particularly strong on lower temperature days.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70250"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12489876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145006550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Power and Sample Size Calculation for Multivariate Longitudinal Trials Using the Longitudinal Rank Sum Test. 用纵向秩和检验计算多变量纵向试验的威力和样本量。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70261

Dhrubajyoti Ghosh, Xiaoming Xu, Sheng Luo

引用次数: 0

Gaussian Process Regression for Value-Censored Functional and Longitudinal Data. 值删减函数和纵向数据的高斯过程回归。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70277

Adam Gorm Hoffmann, Claus Thorn Ekstrøm, Benjamin Zeymer Christoffersen, Andreas Kryger Jensen

{"title":"Gaussian Process Regression for Value-Censored Functional and Longitudinal Data.","authors":"Adam Gorm Hoffmann, Claus Thorn Ekstrøm, Benjamin Zeymer Christoffersen, Andreas Kryger Jensen","doi":"10.1002/sim.70277","DOIUrl":"10.1002/sim.70277","url":null,"abstract":"Gaussian process (GP) regression is widely used for flexible and non-parametric Bayesian modeling of data arising from underlying smooth functions. This paper introduces a solution to GP regression when the observations are subject to value-based censoring. We derive exact and closed-form expressions for the conditional posterior distributions of the underlying functions in both the single-curve fitting case and in the case of a hierarchical model where multiple functions are modeled simultaneously. Our method can accommodate left, right, and interval censoring, and is directly applicable as an empirical Bayes method or integrated in a Markov-Chain Monte Carlo sampler for full posterior inference. The method is validated through extensive simulations, where it substantially outperforms naive approaches that either exclude censored observations or treat them as fully observed values. We give an application to a real-world dataset of longitudinal HIV-1 RNA measurements, where the observations are subject to left censoring due to a detection limit.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70277"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12455908/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmenting Treatment Arms With External Data Through Propensity-Score Weighted Power Priors: An Application in Expanded Access. 利用倾向评分加权权力先验增强外部数据治疗臂：在扩展访问中的应用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-08-01 DOI: 10.1002/sim.70168

Tobias B Polak, Jeremy A Labrecque, Carin A Uyl-de Groot, Joost van Rosmalen

{"title":"Augmenting Treatment Arms With External Data Through Propensity-Score Weighted Power Priors: An Application in Expanded Access.","authors":"Tobias B Polak, Jeremy A Labrecque, Carin A Uyl-de Groot, Joost van Rosmalen","doi":"10.1002/sim.70168","DOIUrl":"https://doi.org/10.1002/sim.70168","url":null,"abstract":"The incorporation of real-world data to supplement the analysis of trials and improve decision-making has spurred the development of statistical techniques to account for introduced confounding. Recently, \"hybrid\" methods have been developed through which measured confounding is first attenuated via propensity scores and unmeasured confounding is addressed through (Bayesian) dynamic borrowing. Most efforts to date have focused on augmenting control arms with historical controls. Here we consider augmenting treatment arms through \"expanded access\", which is a pathway of nontrial access to investigational medicine for patients with seriously debilitating or life-threatening illnesses. Motivated by a case study on expanded access, we developed a novel method (the ProPP) that provides a conceptually simple and easy-to-use combination of propensity score weighting and the modified power prior. Our weighting scheme is based on the estimation of the average treatment effect of the patients in the trial, with the constraint that external patients cannot receive higher weights than trial patients. The causal implications of the weighting scheme and propensity-score integrated approaches in general are discussed. In a simulation study, our method compares favorably with existing (hybrid) borrowing methods in terms of precision and type I error rate. We illustrate our method by jointly analyzing individual patient data from the trial and expanded access program for vemurafenib to treat metastatic melanoma. Our method provides a double safeguard against prior-data conflict and forms a straightforward addition to evidence synthesis methods of trial and real-world (expanded access) data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 18-19","pages":"e70168"},"PeriodicalIF":1.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12380038/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","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 Expectile Joint Model With Varying Coefficient for Longitudinal and Semi-Competing Risks Data. 纵向和半竞争风险数据的变系数贝叶斯期望联合模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-08-01 DOI: 10.1002/sim.70219

Feng Gu, Jiaqing Chen, Jinjing Wang, Yibo Long, Xiaofan Wang, Yangxin Huang

{"title":"Bayesian Expectile Joint Model With Varying Coefficient for Longitudinal and Semi-Competing Risks Data.","authors":"Feng Gu, Jiaqing Chen, Jinjing Wang, Yibo Long, Xiaofan Wang, Yangxin Huang","doi":"10.1002/sim.70219","DOIUrl":"https://doi.org/10.1002/sim.70219","url":null,"abstract":"In the realm of clinical medical research, semi-competing risks data are usually observed in practice, yet there are few studies on the joint models of longitudinal and semi-competing risks data. In this paper, a joint model for longitudinal and semi-competing risks data is proposed. Based on the expectile regression, a linear mixed-effects longitudinal sub-model is formulated, and a Cox proportional hazards survival sub-model is considered under the framework of semi-competing risks. The two sub-models are linked by a shared longitudinal trajectory function. To accommodate the time-varying relationship between the longitudinal response variable and covariates, as well as to introduce flexibility to the structural linkage between longitudinal and survival processes, we incorporate the time-varying coefficients into the joint model in the form of nonparametric functions. The simultaneous Bayesian inference method is utilized to estimate the model parameters, which not only overcomes the convergence problem, but also improves the accuracy of the parameter estimation while effectively reducing the computational burden. The simulation studies are conducted to assess the performance of the proposed joint model and methodology. Finally, we analyze a dataset from the Multicenter AIDS Cohort Study to illustrate the real application of the proposed model and method. In both simulation studies and empirical analyses, joint modeling methods demonstrate performance that meets expected effects.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 18-19","pages":"e70219"},"PeriodicalIF":1.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144795521","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Score Test for Functional Markov Process With Image Predictor. 基于图像预测器的功能马尔可夫过程得分测试。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-08-01 DOI: 10.1002/sim.70231

Yang Wang, Graham A Colditz, Shu Jiang

引用次数: 0

Doubly Robust Estimation of Marginal Cumulative Incidence Curves for Competing Risk Analysis. 竞争风险分析中边际累积发生率曲线的双稳健估计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-08-01 DOI: 10.1002/sim.70066

Patrick van Hage, Saskia le Cessie, Marissa C van Maaren, Hein Putter, Nan van Geloven

{"title":"Doubly Robust Estimation of Marginal Cumulative Incidence Curves for Competing Risk Analysis.","authors":"Patrick van Hage, Saskia le Cessie, Marissa C van Maaren, Hein Putter, Nan van Geloven","doi":"10.1002/sim.70066","DOIUrl":"10.1002/sim.70066","url":null,"abstract":"Covariate imbalance between treatment groups makes it difficult to compare cumulative incidence curves in competing risk analyses. In this paper, we discuss different methods to estimate adjusted cumulative incidence curves, including inverse probability of treatment weighting and outcome regression modeling. For these methods to work, correct specification of the propensity score model or outcome regression model, respectively, is needed. We introduce a new doubly robust estimator, which requires correct specification of only one of the two models. We conduct a simulation study to assess the performance of these three methods, including scenarios with model misspecification of the relationship between covariates and treatment and/or outcome. We illustrate their usage in a cohort study of breast cancer patients estimating covariate-adjusted marginal cumulative incidence curves for recurrence, second primary tumor development, and death after undergoing mastectomy treatment or breast-conserving therapy. Our study points out the advantages and disadvantages of each covariate adjustment method when applied in competing risk analysis.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 18-19","pages":"e70066"},"PeriodicalIF":1.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12333911/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144804863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Dynamic Long-Term Prediction With Intermediate Event Information: A Flexible Model With Bivariate Time-Varying Coefficients. 具有中间事件信息的动态长期预测：一个具有二元时变系数的灵活模型。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-08-01 DOI: 10.1002/sim.70240

Yunyi Wang, Wen Li, Ruosha Li, Jing Ning

{"title":"Dynamic Long-Term Prediction With Intermediate Event Information: A Flexible Model With Bivariate Time-Varying Coefficients.","authors":"Yunyi Wang, Wen Li, Ruosha Li, Jing Ning","doi":"10.1002/sim.70240","DOIUrl":"https://doi.org/10.1002/sim.70240","url":null,"abstract":"The integration of time-to-intermediate event data and the evolving characteristics of patients to enhance long-term prediction has garnered significant interest, driven by the wealth of data generated from longitudinal cohorts. In this paper, we propose sequential/dynamic prediction rules by using regression models with time-varying coefficients. We introduce a class of dynamic models that not only incorporates intermediate event information but also leverages information across different landmark times. To address the challenge of right-censoring, we employ an inverse weighting technique in the estimation process. We establish the asymptotic properties of the estimated parameters and conduct extensive simulations to assess the finite sample performance. Our simulation studies confirm that the proposed method exhibits computational efficiency and yields estimations comparable to those of kernel-based approaches. We apply the proposed method to real-world data from the Atherosclerosis Risk in Communities (ARIC) study and predict mortality while incorporating information regarding a crucial intermediate event, the occurrence of a stroke, and other time-varying covariates dynamically.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 18-19","pages":"e70240"},"PeriodicalIF":1.8,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12374258/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144969755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0