Statistics in Medicine最新文献

Robust Functional Ward's Linkages With Applications in EEG Data Clustering. 鲁棒功能Ward关联及其在脑电数据聚类中的应用。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70771

Tianbo Chen, Wenlin Dai, Hanbing Zhu

引用次数: 0

A Multi-Task Learning Approach for Identifying Prognostic Variables Across Multiple Historical Clinical Trials. 在多个历史临床试验中识别预后变量的多任务学习方法。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70772

Xueying Liu, Huijing Jiang, Jennie Zhang, Xinwei Deng

{"title":"A Multi-Task Learning Approach for Identifying Prognostic Variables Across Multiple Historical Clinical Trials.","authors":"Xueying Liu, Huijing Jiang, Jennie Zhang, Xinwei Deng","doi":"10.1002/sim.70772","DOIUrl":"https://doi.org/10.1002/sim.70772","url":null,"abstract":"In clinical trials, it is critical to incorporate prognostic variables to improve precision for estimating treatment effects. Per FDA guidance, these prognostic variables need to be prospectively specified in the statistical analysis plan. Therefore, there is a great need to effectively identify prognostic variables from historical studies that are similar to the new study. In this work, we propose a multi-task learning method to identify prognostic variables from multiple related historical clinical trials. Specifically, a bi-level constraint variable selection (Bi-CVS) method is developed to identify both study-specific (within-study) and program-level (cross-study) prognostic variables. In addition, we develop an efficient algorithm for the Bi-CVS, tailored for clinical trial settings, and investigate its consistency property. The performance of the proposed method is evaluated through simulations for various endpoints, including continuous, binary, and time-to-event. A real clinical application is also presented to illustrate the proposed method.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70772"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13659928/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184127","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

Randomization-Based Covariate Adjustment for Randomized Block 2 K Factorial Experiments With Missing Baselines. 缺失基线的随机区2k析因实验的随机化协变量调整。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70761

Zixuan Zhao, Yuehan Yang

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Randomization-Based Covariate Adjustment for Randomized Block <ns0:math> <ns0:mrow><ns0:msup><ns0:mn>2</ns0:mn> <ns0:mi>K</ns0:mi></ns0:msup> </ns0:mrow> </ns0:math> Factorial Experiments With Missing Baselines.","authors":"Zixuan Zhao, Yuehan Yang","doi":"10.1002/sim.70761","DOIUrl":"https://doi.org/10.1002/sim.70761","url":null,"abstract":"This paper develops a covariate adjustment framework for randomized block <math> <mrow><msup><mn>2</mn> <mi>K</mi></msup> </mrow> </math> factorial experiments that accommodates high-dimensional covariates and missing baseline measurements. We propose three Lasso-based regression adjustment procedures. Under a randomization-based finite-population framework that allows model misspecification, we show that the resulting Lasso-adjusted estimators are consistent and jointly asymptotically normal. We also provide Neyman-type covariance estimators that support asymptotically valid conservative inference. For the first projection adjustment, the asymptotic variance of each factorial-effect estimator is no larger than that of the corresponding unadjusted estimator. For the joint projection and stratum-specific adjustments, the differences between the asymptotic covariance matrices of the unadjusted and adjusted estimators are positive semidefinite. We also study, within a design-based framework, three strategies for handling missing covariates and provide practical guidance. Efficiency comparisons among these strategies require the linear spaces used in their sparse population projections to be nested. Under this nesting condition, when missingness indicators are informative about potential outcomes, incorporating them as additional adjustment variables can further improve efficiency. None of the three strategies requires specifying parametric outcome or missingness models. Inference is based on randomization and the stated regularity conditions. Single imputation and the missingness-indicator method additionally require treatment-invariant baseline missingness. Simulation studies and an empirical application corroborate the theoretical findings.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70761"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184159","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

A Doubly Robust Approach to Evaluating the Impact of Hospital Length of Stay on Post-Fracture Recovery in Older Adults With Cognitive Impairment. 评估住院时间对认知障碍老年人骨折后康复影响的双重稳健方法

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70768

Haoyu Ren, Tong Wen, Jason Falvey, Yi Huang, Chixiang Chen

{"title":"A Doubly Robust Approach to Evaluating the Impact of Hospital Length of Stay on Post-Fracture Recovery in Older Adults With Cognitive Impairment.","authors":"Haoyu Ren, Tong Wen, Jason Falvey, Yi Huang, Chixiang Chen","doi":"10.1002/sim.70768","DOIUrl":"10.1002/sim.70768","url":null,"abstract":"For older adults diagnosed with Alzheimer's Disease and Related Dementias, recovery outcomes after a hip fracture are often poor, with extended hospital stays and prolonged rehabilitation being common. This paper examines the impact of hospitalization on post-discharge outcomes, focusing on the differential effects of hospital length of stay on recovery, measured by the number of healthy days spent at home (DAH). Our analysis faces important analytical challenges, including baseline confounding, missing outcome, and the complexity of assessing a continuous exposure. To address these issues, we develop a doubly robust estimator that accommodates a continuous exposure and accounts for outcome missingness and provides practical guidance for end-users, including diagnostics for conditional density and missing data models and sensitivity analyses for assumption violation. Extensive simulations demonstrate that our method consistently outperforms existing doubly robust method for continuous exposures when outcome data are missing at random. Application to real-world data suggests that hospital stays longer than 20 days are associated with substantially reduced DAH.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70768"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13672678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196001","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

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70757

François Haguinet, Jeffery L Painter, Gregory E Powell, Andrea Callegaro, Andrew Bate

{"title":"Semantic Similarity-Informed Bayesian Borrowing for Quantitative Signal Detection of Adverse Events.","authors":"François Haguinet, Jeffery L Painter, Gregory E Powell, Andrea Callegaro, Andrew Bate","doi":"10.1002/sim.70757","DOIUrl":"10.1002/sim.70757","url":null,"abstract":"We present a Bayesian dynamic borrowing approach to enhance quantitative identification of adverse events in spontaneous reporting systems. The method embeds a robust meta-analytic predictive prior and incorporates semantic similarity measures (SSMs) to enable weighted information sharing from clinically similar MedDRA preferred terms (PTs) to the target PT. This continuous similarity-based borrowing overcomes limitations of rigid hierarchical grouping in disproportionality analysis (DPA). Using data from the FDA Adverse Event Reporting System and a time-stamped reference set, we evaluate our approach-termed IC SSM-against the current standard approach of \"traditional\" information component (IC) analysis and borrowing at the MedDRA HLGT level (IC HLGT). The primary IC SSM approach at the standard threshold (2.5% posterior quantile <math><mrow><mo>></mo> <mn>0</mn></mrow> </math> ) detected true signals over five months earlier than traditional IC. It showed higher sensitivity (57.0%) but lower precision (14.5%) than traditional IC (50.1%, 16.1%), while outperforming IC HLGT (55.7%, 13.9%). In subsequent analyses, even when tightening the alerting quantile of IC SSM to 1% to improve precision (16.4%), superior sensitivity (51.4%) was retained compared to traditional IC. Similarly, relaxing traditional IC's quantile to 5% yielded only 55.6% sensitivity with 14.2% precision, failing to match IC SSM's performance or timeliness and confirming its inherent advantage. IC SSM's superior timeliness and sensitivity stem from integrating clinically meaningful information, making it a scalable and context-aware enhancement to traditional DPA. The method has potential for exploratory analysis in sparse datasets, and refinements to the borrowing strategies and similarity metrics could yield further performance improvements.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70757"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13673117/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149196530","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

Optimal Two-Phase Sampling Designs for Generalized Raking Estimators With Multiple Parameters of Interest. 多感兴趣参数广义耙估计的最优两相抽样设计。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70786

Jasper B Yang, Bryan E Shepherd, Thomas Lumley, Pamela A Shaw

{"title":"Optimal Two-Phase Sampling Designs for Generalized Raking Estimators With Multiple Parameters of Interest.","authors":"Jasper B Yang, Bryan E Shepherd, Thomas Lumley, Pamela A Shaw","doi":"10.1002/sim.70786","DOIUrl":"10.1002/sim.70786","url":null,"abstract":"Large observational datasets, including those derived from electronic health records, are a valuable resource for medical research but are often affected by missingness, measurement error, and misclassification. Two-phase sampling with generalized raking (GR) estimation is an efficient and robust approach to statistical inference in such settings. In this approach, variables that are unavailable or measured with error in a large Phase 1 cohort are obtained with higher-quality measurements in a Phase 2 subsample. Previous research has studied optimal Phase 2 sampling designs for inverse-probability-weighted (IPW) estimators in nonadaptive, multiparameter settings, and for GR estimators in single-parameter settings. In this work, we extend these results by deriving optimal adaptive, multiwave sampling designs for IPW and GR estimators when multiple parameters are of interest. We propose several practical allocation strategies, examine the design choices that arise when implementing them, and evaluate their performance through extensive simulations and an example using data from people living with HIV attending the Vanderbilt Comprehensive Care Clinic (VCCC). Our results show that independently optimizing allocation for each parameter improves efficiency over traditional case-control sampling. We also derive an integer-valued, A-optimal allocation method that typically outperforms independent optimization. Notably, we find that optimal designs for GR can differ substantially from those for IPW, and that this distinction can meaningfully affect estimator efficiency in the multiple-parameter setting. These findings offer practical guidance for future two-phase studies involving incomplete or error-prone data.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70786"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13675120/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149207927","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

Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial-Eligible Individuals. 将条件平均治疗效应从嵌套随机试验推广到符合试验条件的个体。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70767

Lan Wen, Issa J Dahabreh, Yu-Han Chiu

{"title":"Generalizing Conditional Average Treatment Effects From Nested Randomized Trials to Trial-Eligible Individuals.","authors":"Lan Wen, Issa J Dahabreh, Yu-Han Chiu","doi":"10.1002/sim.70767","DOIUrl":"10.1002/sim.70767","url":null,"abstract":"Randomized controlled trials often enroll participants whose characteristics differ from those of a target population. When the distribution of effect modifiers differs between trial participants and the target population, treatment effects estimated within the trial may not be directly applicable to all trial-eligible individuals. Although substantial progress has been made in generalizing average treatment effects from randomized trials to target populations, clinical and policy decisions are often informed by conditional average treatment effects (CATEs) that characterize heterogeneity across selected effect modifiers in the target population. In this work, we provide a unified approach for estimating CATEs in a target population of trial-eligible individuals under a nested trial design. Our approach first estimates nuisance functions using data-adaptive methods and constructs pseudo-outcomes from conditional influence functions. These pseudo-outcomes are subsequently regressed on pre-specified effect modifiers using local linear (kernel) regression to obtain a smoothed estimate of the CATE function. We establish asymptotic properties under sample splitting and cross-fitting, and provide conditions under which first-stage nuisance estimation has negligible impact on the final estimator. We assess the finite-sample performance of the proposed estimator via simulation studies, and illustrate its practical use through an application to the Coronary Artery Surgery Study (CASS).","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70767"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13652670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149148701","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

Simulated Annealing for Model-Robust Partial Profile Choice Designs in Healthcare Preference Studies. 医疗偏好研究中模型-鲁棒部分轮廓选择设计的模拟退火。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70770

Yicheng Mao, Roselinde Kessels

{"title":"Simulated Annealing for Model-Robust Partial Profile Choice Designs in Healthcare Preference Studies.","authors":"Yicheng Mao, Roselinde Kessels","doi":"10.1002/sim.70770","DOIUrl":"10.1002/sim.70770","url":null,"abstract":"Discrete Choice Experiments (DCEs) investigate participants' preferences by observing their choice behavior in hypothetical scenarios and are widely used in the domain of healthcare. To reduce participants' cognitive burden, especially when dealing with a large number of attributes, researchers often employ partial profile designs. In these designs, certain attributes within each choice set are kept constant. Current literature on partial profile designs mainly focuses on main-effects models rather than interaction-effect models, with certain partial profile designs even incapable of estimating interaction effects. To address this issue, this paper introduces a Simulated Annealing (SA) algorithm to construct partial profile designs based on an interaction-effects model. During the experimental design phase, the existence and magnitude of interaction effects are often unknown. Therefore, this paper proposes a model-robust experimental design strategy. Through extensive simulation experiments and a real-life case study, we demonstrate that our SA model-robust partial profile design performs relatively well regardless of the underlying model.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70770"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13661791/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149184149","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

Valid Inference for Two Sample Summary Statistics Mendelian Randomization. 两样本汇总统计孟德尔随机化的有效推断。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70784

Kai Wang, Grace Z Wang

{"title":"Valid Inference for Two Sample Summary Statistics Mendelian Randomization.","authors":"Kai Wang, Grace Z Wang","doi":"10.1002/sim.70784","DOIUrl":"10.1002/sim.70784","url":null,"abstract":"Mendelian randomization (MR) studies increasingly rely on summary statistics from genome-wide association studies (GWASs). However, current summary-data methods treat marginal GWAS effect estimates as independent across instrumental single-nucleotide polymorphisms (SNPs), which holds only when the SNPs have no effect. Without imposing this independence assumption, we derive expressions for the two-stage least squares (TSLS), two-sample TSLS (TSTSLS), and generalized method-of-moments estimators together with their variance estimators using only GWAS summary statistics, assuming the instrumental SNPs are in linkage equilibrium. We show that the widely used inverse-variance weighted (IVW) estimator is generally not equivalent to either TSLS or TSTSLS, contrary to a common claim in the MR literature. We further demonstrate that the conventional IVW standard error is generally inconsistent and provide a consistent alternative. Extensive simulations confirm the theoretical results and reveal that Wald confidence intervals based on summary statistics can be anti-conservative. An application to publicly available GWAS summary statistics on body mass index and systolic blood pressure illustrates the practical implications of the proposed methodology.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70784"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220645","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

Calibration of Priors for Bayesian Model-Based Dose-Finding Trial Designs With Joint Outcomes. 具有联合结果的基于贝叶斯模型的剂量寻找试验设计的先验校正。

IF 2.1 4区医学

Statistics in Medicine Pub Date : 2026-10-01 DOI: 10.1002/sim.70746

Emily Alger, Shing M Lee, Ying Kuen K Cheung, Christina Yap

{"title":"Calibration of Priors for Bayesian Model-Based Dose-Finding Trial Designs With Joint Outcomes.","authors":"Emily Alger, Shing M Lee, Ying Kuen K Cheung, Christina Yap","doi":"10.1002/sim.70746","DOIUrl":"10.1002/sim.70746","url":null,"abstract":"The goal of dose-finding oncology trials is to assess the safety of anti-cancer treatments across multiple doses and to recommend dose(s) for subsequent trials. As patients' outcomes accrue, trialists dynamically recommend new doses for further investigation during the trial. This adaptive decision-making lends itself to Bayesian learning, with Bayesian frameworks increasingly guiding dose recommendations in model-based dose-finding designs, including the Continual Reassessment Method (CRM). However, such approaches introduce increased complexity, not least when additional outcomes are incorporated within designs. Such trial designs require careful prior selection. Directly applying prior calibration methods developed for single outcome model-based trial designs to joint outcome trial designs may introduce unintended bias in dose recommendations, potentially limiting dose exploration and failing to accurately reflect trialists' a priori beliefs. We extend methodology to analytically calibrate priors for model-based joint outcome trial designs with divergence minimisation. Our method offers an analytical and computationally efficient technique. We demonstrate the flexibility of our calibration method relative to existing approaches in ensemble simulation scenarios, and show that calibrating priors in this way delivers improved accuracy and computational efficiency compared with traditional grid search methods. As Bayesian dose-finding trial designs continue to advance, research and guidance on the effective calibration of design parameters is essential to support uptake and ensure optimal performance in practice. This method provides an analytical and intuitive approach to prior calibration, highlighting the importance of rigorous prior calibration in improving model accuracy and dose selection for safer, more effective oncology treatments.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 23-24","pages":"e70746"},"PeriodicalIF":2.1,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628446/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115075","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