Statistics in Medicine最新文献_第2页

Win Ratio as an Effect Size Measure Under Non-Proportional Hazards: A Comparison With Difference in Restricted Mean Survival. 胜率作为非比例风险下的效应大小度量：与受限平均生存差异的比较。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70554

Yuan Wu, Xiaofei Wang, Zhiguo Li

{"title":"Win Ratio as an Effect Size Measure Under Non-Proportional Hazards: A Comparison With Difference in Restricted Mean Survival.","authors":"Yuan Wu, Xiaofei Wang, Zhiguo Li","doi":"10.1002/sim.70554","DOIUrl":"https://doi.org/10.1002/sim.70554","url":null,"abstract":"When the proportional hazards assumption does not hold, the hazard ratio can misrepresent treatment effects in survival analysis. We evaluate the win ratio, originally proposed for prioritizing multiple outcomes, as an effect size measure for a single survival outcome under non-proportional hazards, and compare it with the difference in restricted mean survival time (RMST). We perform bootstrap-based inference for the win ratio under both right- and interval-censoring using plug-in estimators based on nonparametric maximum likelihood estimators or spline-based sieve maximum likelihood estimators of the survival functions. We also study stratified win ratio to mitigate confounding. Extensive simulations are conducted to assess and compare the performances of the win ratio and the difference in RMST under various types of alternatives encountered in practice. The simulation results show that the win ratio-based tests outperform RMST-based tests when treatment benefits arise early, whereas RMST is more sensitive to late-onset effects, and stratified win ratio maintains nominal type I error in the presence of confounding, unlike unstratified win ratio. As an illustration, we analyze right-censored and interval-censored progression-free survival in patients with multiple myeloma treated with two different regimens. The results of this article support reporting the win ratio, along with the difference in RMST, when the proportional hazards assumption is doubtful, offering complementary clinical interpretability and robustness across censoring mechanisms and treatment effect patterns.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70554"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781604","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

Sequential Multiple Assignment Randomized Trials Based on Restricted Mean Survival Time. 基于限制平均生存时间的序贯多任务随机试验。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70563

Jianhong Pan, Shijie Yu, Minggang Yin, Yuxuan Yang, Chongyang Duan

{"title":"Sequential Multiple Assignment Randomized Trials Based on Restricted Mean Survival Time.","authors":"Jianhong Pan, Shijie Yu, Minggang Yin, Yuxuan Yang, Chongyang Duan","doi":"10.1002/sim.70563","DOIUrl":"https://doi.org/10.1002/sim.70563","url":null,"abstract":"Sequential multiple assignment randomized trials (SMART) designs, which are used to evaluate adaptive treatment strategies (ATSs), involve multiple stages of patient randomization based on intermediate outcomes. While offering greater flexibility and personalization in treatment, these designs are often analytically complex and resource-intensive-especially when survival outcomes are involved, given the extended follow-up times and potential violations of proportional hazards assumptions. To address these challenges, we propose a statistical inference framework based on restricted mean survival time (RMST). RMST does not rely on the proportional-hazards assumption and serves as a robust summary for survival data. The framework includes fixed- and dynamic-weight RMST estimators, their variance-covariance structures, confidence intervals, and hypothesis tests for pairwise and global comparisons among ATSs. We also integrate interim analyses into SMART designs using RMST and develop a type I error-control method that accommodates the lack of the independent-increments property. Extensive simulations demonstrate good estimator performance and show that interim designs can reduce either sample size or trial duration. In summary, this study offers an efficient and practical framework for SMART trials with survival outcomes.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70563"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781637","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

Predicting Win-Loss Probabilities for Composite Time-to-Event Outcomes Under The Proportional Win-Fractions Regression Model. 在比例赢-分数回归模型下预测复合时间-事件结果的输赢概率。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70569

Lu Mao

{"title":"Predicting Win-Loss Probabilities for Composite Time-to-Event Outcomes Under The Proportional Win-Fractions Regression Model.","authors":"Lu Mao","doi":"10.1002/sim.70569","DOIUrl":"10.1002/sim.70569","url":null,"abstract":"For composite time-to-event outcomes, the win ratio as a relative measure ignores ties resulting from non-occurrence of events, which can obscure important context in regression settings where event rates-and hence the proportion of ties-vary over time and across covariate values. To gain a more complete understanding of covariate effects, we propose coupling the proportional win-fractions (PW) model, which specifies only the win ratio, with a time-to-first-event model (e.g., Cox model), from which the tie probability can be inferred. This combination enables prediction of time-dependent win and loss probabilities on an absolute scale for any given pair of covariate profiles, with uncertainty quantified through robust variance estimation, and facilitates inference on tie-adjusted measures such as the net benefit and win odds to complement the win ratio in evaluating effect size. Residual-based diagnostics further allow refinement of model fit through appropriate covariate specification or stratification to address potential violations of proportionality assumptions. Through simulation studies and an application to the landmark HF-ACTION trial, we demonstrate that the proposed approach provides accurate and clinically interpretable predictions when model assumptions are approximately satisfied. These predictions reveal a pattern of diminishing returns on absolute win-loss probabilities as a key baseline biomarker increases across the population despite a constant win ratio. We also show that violations of the proportional win-fractions assumption can lead to biased predictions, underscoring the importance of model diagnostics. When the primary objective is to characterize time-varying covariate effects on win-loss probabilities, more flexible modeling approaches may be warranted. In addition to a small code example in the paper, the full methodology is implemented in the WR package, available on GitHub ( https://lmaowisc.github.io/WR/) and the Comprehensive R Archive Network (CRAN).","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70569"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13124461/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781590","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

Variable Selection in Mixed-Effects Location-Scale and Location-Shift Models. 位置-尺度和位置-位移混合效应模型中的变量选择。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70553

Moritz Berger, Maria Iannario

{"title":"Variable Selection in Mixed-Effects Location-Scale and Location-Shift Models.","authors":"Moritz Berger, Maria Iannario","doi":"10.1002/sim.70553","DOIUrl":"10.1002/sim.70553","url":null,"abstract":"When ordinal responses to questionnaires structured on the basis of Likert scales show differing variability or heterogeneity in subgroups of the population, appropriate regression approaches that are able to take this issue into account are the location-scale and location-shift model. If data come in clusters, which causes within-cluster variance, an additional cluster-level random effect specification is due. Cumulative models for ordinal responses are considered assessing the responses in terms of mean level (or location), variability (or scale), heterogeneity (or dispersion) and in terms of random effects related to clusters. Furthermore, in order to reduce the complexity of the models, a variable selection procedure through adaptive fused LASSO-type regularization is proposed. A case study with data from the Survey of Health, Ageing and Retirement in Europe is used to demonstrate the applicability of the models and the properties of the selection procedures. It is shown that variable selection by regularization produces stable parameter estimates and results that are easy to interpret in all model components. The performance of the proposed regularization approach is further assessed by means of a simulation study.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70553"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13125975/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781642","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

A Novel Group Sequential Design for Sequential Multiple Assignment Randomized Trial. 一种新的序贯多任务随机试验组序设计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70564

Xueqing Liang, Shijie Yu, Minggang Yin, Siyu Zhu, Yixin Luo, Yuxuan Yang, Chongyang Duan

引用次数: 0

Penalized GEE for Complex Carry-Over in Repeated-Measures Crossover Designs. 在重复测量交叉设计中，因复杂的携带而惩罚GEE。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70561

Nelson Alirio Cruz, Oscar Orlando Melo, Kalliopi Mylona

{"title":"Penalized GEE for Complex Carry-Over in Repeated-Measures Crossover Designs.","authors":"Nelson Alirio Cruz, Oscar Orlando Melo, Kalliopi Mylona","doi":"10.1002/sim.70561","DOIUrl":"https://doi.org/10.1002/sim.70561","url":null,"abstract":"Crossover designs are commonly employed in clinical and behavioral research, yet the statistical models used to analyze them often rely on unrealistic assumptions-either ignoring carry-over effects or modeling them as simple and homogeneous across treatment sequences. However, carry-over effects are frequently complex, varying by treatment order and interaction, and until now, no statistical methodology had been formally established to estimate such complex effects. This paper introduces a penalized semiparametric Generalized Estimating Equations (GEE) approach designed to estimate first order complex carry-over effects in crossover designs with repeated measurements. We first derive identifiability conditions under which complex carry-over effects become estimable. We then provide theoretical guarantees-building on an extension of the sandwich variance formula-showing that the proposed penalized estimator achieves asymptotic normality for the functional components and shrinks negligible carry-over effects toward zero, thereby enabling their practical identification. Through simulation studies and application to real data, the methodology demonstrates improved estimation accuracy when complex carry-over effects are present, outperforming models that assume simple or no carry-over. This work represents the first rigorous and generalizable approach for modeling complex carry-over effects in repeated-measures crossover designs.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70561"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781606","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

Delayed Exposures and Pre-Exposure Periods in Self-Controlled Case Series Studies. 自我控制病例系列研究中的延迟暴露和暴露前时间。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70566

Heather Whitaker, Yonas Ghebremichael Weldeselassie, Paddy Farrington

引用次数: 0

A Modified Normalized Power Prior Approach for Bayesian Adaptive Borrowing in Item Response Theory Models. 项目反应模型贝叶斯自适应借阅的改进归一化幂先验方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70568

Qiang Zhang, Wei Xiong, Min Wang, Keying Ye, Zifei Han

{"title":"A Modified Normalized Power Prior Approach for Bayesian Adaptive Borrowing in Item Response Theory Models.","authors":"Qiang Zhang, Wei Xiong, Min Wang, Keying Ye, Zifei Han","doi":"10.1002/sim.70568","DOIUrl":"10.1002/sim.70568","url":null,"abstract":"Questionnaires are widely used in clinical research and mental health studies, but their responses are usually more subjective than clinical biomarkers. Researchers may use latent variable models such as the item response theory (IRT) model to characterize individual ability, but response instability and small sample sizes can lead to increased uncertainty in estimation. Historical data from similar questionnaires may help reduce variation, but they must be incorporated adaptively. The challenge is further compounded by the need to ensure computational efficiency in IRT models with a large number of parameters. In this work, we develop a Bayesian framework for adaptive borrowing in IRT models based on an approximated normalized power prior (NPP) that treats the borrowing weight as a random parameter. The proposed method makes NPP feasible for general IRT models and is coupled with a full Bayesian data augmentation strategy that enables joint estimation of ability and item parameters through an efficient Gibbs sampler. In simulations, the approach adaptively increases borrowing when historical and current data are compatible and automatically downweights conflicting information. Relative to analyzes without borrowing, the method reduces variance and mean squared error while maintaining coverage across a range of test lengths, item discrimination profiles, and historical-current concordance scenarios. We illustrate the method by integrating historical mental health surveys into current assessments, yielding more precise ability estimates with preserved calibration. An efficient implementation is provided in our updated package NPP available on the Comprehensive R Archive Network.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70568"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781142","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

Defining Utility as a Measure of Preference Under Uncertainty in Phase I-II Oncology Dose Finding Trials. 在I-II期肿瘤剂量发现试验中，将效用定义为不确定条件下的偏好度量。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70547

Andrew Hall, Duncan Wilson, Stuart Barber, Sarah R Brown

{"title":"Defining Utility as a Measure of Preference Under Uncertainty in Phase I-II Oncology Dose Finding Trials.","authors":"Andrew Hall, Duncan Wilson, Stuart Barber, Sarah R Brown","doi":"10.1002/sim.70547","DOIUrl":"10.1002/sim.70547","url":null,"abstract":"The main objective of dose finding trials is to find an optimal dose amongst a candidate set for further research. The trial design in oncology proceeds in stages with a decision as to how to treat the next group of patients made at every stage until a final sample size is reached or the trial stopped early. This work applies a Bayesian decision-theoretic approach to the problem, proposing a new utility function based on both efficacy and toxicity and grounded in von Neumann-Morgenstern (VNM) utility theory. Our proposed framework seeks to better capture real clinical judgments by allowing attitudes to risk to vary when the judgments are of gains or losses, which are defined with respect to an intermediate outcome known as a reference point. We call this method Reference Dependent Decision Theoretic dose finding (R2DT). A simulation study demonstrates that the framework can perform well and produce good operating characteristics. The simulation results demonstrate that R2DT is better at detecting the optimal dose in scenarios where candidate doses are around minimum acceptable efficacy and maximum acceptable toxicity thresholds. The proposed framework shows that a flexible utility function, which better captures clinician beliefs, can lead to trials with good operating characteristics, including a high probability of finding the optimal dose. Our work demonstrates proof-of-concept for this framework, which should be evaluated in a broader range of settings.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70547"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13130384/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147781492","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

Innovative Clinical Trial Approach for Evaluating Digital Medical Devices Under European Fast-Track Regulatory Frameworks. 在欧洲快速通道监管框架下评估数字医疗器械的创新临床试验方法。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2026-05-01 DOI: 10.1002/sim.70572

Moreno Ursino, Sandrine Boulet, Corinne Collignon, Florence Francis-Oliviero, Edouard Lhomme, Raphaël Porcher, Florence Saillour, Gaël Varoquaux, Vincent Vercamer, Rodolphe Thiébaut, Sarah Zohar

{"title":"Innovative Clinical Trial Approach for Evaluating Digital Medical Devices Under European Fast-Track Regulatory Frameworks.","authors":"Moreno Ursino, Sandrine Boulet, Corinne Collignon, Florence Francis-Oliviero, Edouard Lhomme, Raphaël Porcher, Florence Saillour, Gaël Varoquaux, Vincent Vercamer, Rodolphe Thiébaut, Sarah Zohar","doi":"10.1002/sim.70572","DOIUrl":"https://doi.org/10.1002/sim.70572","url":null,"abstract":"To address patient demand for rapid access to innovative digital medical devices (DMDs), several health technology assessment (HTA) authorities in European Union countries provide transitional or provisional access and reimbursement pathways. These pathways are available when only incomplete clinical trial data are accessible, and significant uncertainty remains regarding the clinical benefits, even after CE (European conformity) marking has been obtained. Once manufacturers complete the clinical studies, additional real-world data (RWD) may become available as a result of the device's use in the target population. Consequently, regulators can draw on both sources of information to support their final decision-making processes. For a statistically principled evaluation of such settings, we propose a statistical framework suitable for DMD evaluation under European HTA fast-track requirements, integrating both clinical trial data and RWD. The framework consists of three key steps: (1) an interim analysis of clinical trial data, which can support temporary regulatory authorization and enable the collection of RWD; (2) a final analysis of the clinical trial data; and (3) a meta-analysis combining the clinical trial data and RWD, contingent upon obtaining temporary authorization. To optimize the timing of the interim analysis and the application for temporary authorization, we introduce several metrics. The proposed framework was assessed by means of an extensive simulation study. This framework should be complemented by a post-market evaluation of the DMD once it has been widely adopted, aligning with the principles of phase IV studies.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"45 10-12","pages":"e70572"},"PeriodicalIF":1.8,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13143562/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147842769","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