Statistics in Medicine最新文献

A Bayesian Causal Model for Matrix-Valued Exposures With Applications to Radiotherapy Planning. 矩阵值照射的贝叶斯因果模型及其在放疗计划中的应用。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70559

Zijin Liu, Zhihui Amy Liu, Jennifer Dang, Charles Catton, Himanshu R Lukka, Peter Chung, Olli Saarela

{"title":"A Bayesian Causal Model for Matrix-Valued Exposures With Applications to Radiotherapy Planning.","authors":"Zijin Liu, Zhihui Amy Liu, Jennifer Dang, Charles Catton, Himanshu R Lukka, Peter Chung, Olli Saarela","doi":"10.1002/sim.70559","DOIUrl":"https://doi.org/10.1002/sim.70559","url":null,"abstract":"In radiotherapy for cancer, organs surrounding the target tumor, known as organs-at-risk (OARs), should be protected from excessive radiation to avoid toxicity. Radiation exposure to multiple OARs can be summarized using matrix-valued dose-volume histograms (DVH), and understanding the causal relationship between DVHs and toxicity outcomes can improve treatment planning. Conventional causal models are not tailored to high-dimensional, highly correlated matrix-valued data. In this paper, we propose a Bayesian three-component joint model for a matrix-valued DVH exposure with a causal interpretation. Dimension reduction is achieved via multilinear principal component analysis (MPCA), which extracts information from matrices more efficiently than conventional PCA. A Hamiltonian Monte Carlo algorithm is adapted for estimation. We demonstrate the model's performance in estimating average causal effects through simulations. For interpretation, we map dose effects back to the original DVH matrix, illustrating that our model can correctly identify relevant effects in both simulation and application studies.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70559"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109004/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147780856","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

Improving Power of the Win Ratio Analysis through Distance-based Weights. 通过基于距离的权重提高胜率分析的能力。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70562

Md Rejuan Haque, Madison Hyer, Lai Wei, Xueliang Pan, Guy Brock

引用次数: 0

Illustrating Implications of Misaligned Causal Questions and Statistics in Settings With Competing Events and Interest in Treatment Mechanisms. 说明在竞争事件和治疗机制的兴趣设置中不一致的因果问题和统计的含义。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70535

Takuya Kawahara, Sean McGrath, Jessica G Young

{"title":"Illustrating Implications of Misaligned Causal Questions and Statistics in Settings With Competing Events and Interest in Treatment Mechanisms.","authors":"Takuya Kawahara, Sean McGrath, Jessica G Young","doi":"10.1002/sim.70535","DOIUrl":"https://doi.org/10.1002/sim.70535","url":null,"abstract":"In the presence of competing events, many investigators are interested in a direct treatment effect on the event of interest that does not capture treatment effects on competing events. Classical survival analysis methods that treat competing events like censoring events, at best, target a controlled direct effect: the effect of the treatment under a difficult to imagine and typically clinically irrelevant scenario where competing events are somehow eliminated. A separable direct effect, quantifying the effect of a future modified version of the treatment, is an alternative direct effect notion that may better align with an investigator's underlying causal question. In this paper, we provide insights into the implications of naively applying an estimator constructed for a controlled direct effect (i.e., \"censoring by competing events\") when the actual causal effect of interest is a separable direct effect. We illustrate the degree to which controlled and separable direct effects may take different values, possibly even different signs, and the degree to which these two different effects may be differentially impacted by violation and/or near violation of their respective identifying conditions under a range of data generating scenarios. Finally, we provide an empirical comparison of inverse probability of censoring weighting to an alternative weighted estimator specifically structured for a separable effect using data from a randomized trial of estrogen therapy and prostate cancer mortality.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70535"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13109003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781539","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

Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model. 基于监督知识引导贝叶斯因子模型的多组学和临床结果数据的同步表示学习。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70570

Qiyiwen Zhang, Changgee Chang, Chong Jin, Li Shen, Qi Long

{"title":"Simultaneous Representation Learning of Multi-Omics and Clinical Outcome Data via a Supervised Knowledge-Guided Bayesian Factor Model.","authors":"Qiyiwen Zhang, Changgee Chang, Chong Jin, Li Shen, Qi Long","doi":"10.1002/sim.70570","DOIUrl":"https://doi.org/10.1002/sim.70570","url":null,"abstract":"With the advent of high-throughput techniques, multi-omics data and various clinical outcomes have been collected for a range of diseases. Multi-omics data play a crucial role in uncovering complex biological processes, yet simultaneous representation learning of such high-dimensional, heterogeneous multi-modality data along with clinical outcomes remains limited. To address this gap, we propose a supervised knowledge-guided Bayesian factor model for integrative analysis of multi-omics and clinical outcome data. The proposed method simultaneously extracts an informative low-dimensional representation and predicts one or more clinical outcomes of interest. The two-level adaptive shrinkage in the novel hierarchical priors allows for the identification of both active modalities and features, resulting in a biologically meaningful structural identification of the high-dimensional data. Moreover, the method is robust to noisy edges in biological graphs that do not align with ground truth. Finally, the proposed method can handle different data types including both continuous and categorical data. Extensive simulation studies and real data analyses of Alzheimer's disease (AD) data demonstrate the advantages of the proposed approach over existing methods. Notably, our analysis of multi-omics and imaging phenotype data from ADNI provides meaningful insights into the underlying biological mechanisms of AD.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70570"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13110451/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781586","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

Adaptive Designs in Trials With Time-to-Event Endpoints and Covariate Adjustment. 具有时间-事件终点和协变量调整的试验中的自适应设计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70577

Daniel Backenroth, Ting Ye

{"title":"Adaptive Designs in Trials With Time-to-Event Endpoints and Covariate Adjustment.","authors":"Daniel Backenroth, Ting Ye","doi":"10.1002/sim.70577","DOIUrl":"10.1002/sim.70577","url":null,"abstract":"We investigate adaptive designs that can be used to take advantage of increases in efficiency from the use of the covariate-adjusted log-rank test in trials with time-to-event endpoints. These adaptive designs are intended to address a key practical challenge in taking advantage of efficiency gains from this test, which is that the actual efficiency gain attained in a trial may differ from estimates of the efficiency gain at the design stage. We evaluate information-based interim monitoring and blinded event target adjustment (BETA) as tools for improving efficiency, focusing on statistical and operational trade-offs between these approaches. We show using two different data-generating processes that regression coefficients used in the construction of the covariate-adjusted log-rank test may increase over time. As a result, variance reductions from adjustment with the covariate-adjusted log-rank test may also increase with additional follow-up in the trial. This means that BETA, which estimates variance reductions at an interim time-point in order to decide the timing of interim and final analyses, may not fully take advantage of the attainable efficiency gains with covariate adjustment. Simulations that incorporate repeated testing compare trial designs in terms of power, duration, and sample size reduction. While information-based monitoring enables faster analyses when covariates are highly prognostic, it may be operationally burdensome. BETA offers logistical simplicity but may not fully realize the potential efficiency gains from covariate adjustment. Reducing sample size with either adaptive design is potentially risky, as losses from a longer trial duration arising from over-optimistic estimates of efficiency gains at the design stage may be much greater than savings from a smaller trial.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70577"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781454","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

Correction to "Structure Identification, Estimation for Variable Selection for Varying Coefficient EV Models With Longitudinal Data". 对“纵向数据变系数EV模型的结构识别、变量选择估计”的修正。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70565

引用次数: 0

Joint Frailty Mixture Cure Model for Recurrent Event Data With Dependent Censoring: An MCEM Approach. 复发事件数据的联合脆弱混合治疗模型：一种MCEM方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70579

Nasrin Sultana, Moudud Alam, Md Hasinur Rahaman Khan

{"title":"Joint Frailty Mixture Cure Model for Recurrent Event Data With Dependent Censoring: An MCEM Approach.","authors":"Nasrin Sultana, Moudud Alam, Md Hasinur Rahaman Khan","doi":"10.1002/sim.70579","DOIUrl":"https://doi.org/10.1002/sim.70579","url":null,"abstract":"Advancements in modern medical technology have enabled cures for a fraction of patients while extending survival times for those who are not cured. For non-cured patients, disease recurrence is influenced by observed covariates and unobserved individual heterogeneity (random effects). In biomedical studies, dependent censoring is frequently encountered, for example, in cancer patients, where right censoring can be caused by death from unrelated diseases or due to an (unobservable) cure status. This study introduces a joint frailty model for recurrent event data with a cure fraction, effectively capturing heterogeneity and inducing dependent censoring. The proposed multivariate joint frailty mixture cure models incorporate covariates and frailties, together with the event incidence time and latent cure status. The model accounts for the probability of a cure after each recurrence using both the complementary log-log and the logistic link function. A likelihood-based estimation method is developed using the Monte Carlo Expectation-Maximization (MCEM) algorithm. Through Monte Carlo simulation, we examine the finite sample properties of the MCEM estimators, supplemented by a real-world application using secondary data on hospital readmissions for colorectal cancer recurrence post-surgery. Simulation results suggest lifetime and frailty parameter estimates are unbiased and consistent. Compared to models with identical frailty structure, both the complementary log-log and the logistic cure frailty models with dependent frailties demonstrate a better fit with the real data, as evidenced by lower Akaike information criteria values.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70579"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842914","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

Sample Size Determination for Comparing Slopes in Cluster Randomized Trials With Longitudinal Measurements. 用纵向测量方法比较聚类随机试验中斜率的样本量测定。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70576

Jijia Wang, Song Zhang, Chul Ahn

{"title":"Sample Size Determination for Comparing Slopes in Cluster Randomized Trials With Longitudinal Measurements.","authors":"Jijia Wang, Song Zhang, Chul Ahn","doi":"10.1002/sim.70576","DOIUrl":"https://doi.org/10.1002/sim.70576","url":null,"abstract":"In cluster randomized trials with longitudinal measurements (CRTLMs), clusters of subjects, rather than individual subjects, are randomly assigned to either control or intervention groups. Measurements are collected from these subjects repeatedly at prespecified times until the end of the study. In clinical research, the focus is typically on investigating trends or progress over time to evaluate the effectiveness of a new treatment or track disease progression, rather than solely analyzing mean values or endpoint measurements. For comparing slopes between two groups, we have derived closed-form sample size formulas based on the generalized estimating equation (GEE) approach under independence working correlation. Our proposed method is highly flexible, allowing for the incorporation of unbalanced randomization, arbitrary correlation structures, various missing data scenarios through observational probabilities and missing patterns, and variability in cluster sizes. This flexibility provides a practical and robust sample size solution for CRTLMs. Simulation studies demonstrate that the proposed method performs well, maintaining empirical power and type I error rate close to their nominal values. Additionally, we illustrate the application of our method using a real clinical trial, showcasing its practical utility in real-world implementation.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70576"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13138864/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842870","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

Proportional Hazards Regression for Interval-Censored Outcomes With an Interval-Censored Covariate. 含区间截尾协变量的区间截尾结果的比例风险回归。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70573

Dongdong Li, Yue Song, Wenbin Lu, Huldrych F Günthard, Roger Kouyos, Rui Wang

{"title":"Proportional Hazards Regression for Interval-Censored Outcomes With an Interval-Censored Covariate.","authors":"Dongdong Li, Yue Song, Wenbin Lu, Huldrych F Günthard, Roger Kouyos, Rui Wang","doi":"10.1002/sim.70573","DOIUrl":"10.1002/sim.70573","url":null,"abstract":"Identifying predictors for viral rebound trajectories after antiretroviral therapy (ART) interruption is central to HIV cure research. Motivated by the need to determine whether the time to achieve viral suppression after ART initiation can predict the time to viral rebound following ART interruption, we investigate modeling approaches that relate an interval-censored outcome (e.g., time to viral rebound) and an interval-censored covariate (e.g., time to viral suppression) under the assumption that viral load only crosses a threshold when bracketed by consecutive assessments. We develop estimation and inference procedures for fitting a proportional hazards regression model when both the outcome and a covariate are interval-censored, without imposing parametric assumptions on the baseline hazard functions. To accommodate participants with multiple episodes of ART initiation and interruption, we extend the proposed method to account for the clustering of repeated observations within individuals. We derive the asymptotic properties of the proposed method and evaluate its finite-sample performance through simulation studies. Applying the method to data from the Zurich Primary HIV Infection cohort, we find that a longer time to viral suppression during ART is associated with an increased hazard of viral rebound after ART interruption.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70573"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13136924/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147821007","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

When Better is Worse: A Paradox of the Win Ratio and Net Treatment Benefit. 当好即坏：胜率和净治疗收益的悖论。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70580

Valerie R Fu

引用次数: 0