Statistical Methods in Medical Research最新文献_第2页

Multiple-use prediction and calibration for all future values: Exact simultaneous tolerance bands for multiple and polynomial regressions. 所有未来值的多用途预测和校准：多重和多项式回归的精确同时公差带。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-09-08 DOI: 10.1177/09622802261470084

Lingjiao Wang, Yang Han, Wei Liu, Frank Bretz

{"title":"Multiple-use prediction and calibration for all future values: Exact simultaneous tolerance bands for multiple and polynomial regressions.","authors":"Lingjiao Wang, Yang Han, Wei Liu, Frank Bretz","doi":"10.1177/09622802261470084","DOIUrl":"10.1177/09622802261470084","url":null,"abstract":"Multiple-use prediction and calibration for all future values play an important role in many areas, including health and medical research. Simultaneous tolerance bands (STBs) can be employed for these purposes. This article studies the construction of exact STBs for multiple regression over any given rectangular covariate regions and for polynomial regression over any given covariate intervals. We first fill the gap in constructing the exact STBs for multiple regression over rectangular regions. We then propose a general form of STBs and develop a method for computing the critical constants for all STBs considered, including existing ones in the literature. A new STB is also proposed for both multiple and polynomial regressions. This new STB and several existing forms are then compared under the average shift (AS) criterion. Numerical results indicate that the new STB performs best under the AS criterion and is therefore recommended. In addition, a computationally efficient algorithm is presented. Real-life examples are provided for illustration.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2076-2094"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13646414/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148913666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Corrigendum to "Design and analysis of factorial clinical trials: The impact of one treatment's effectiveness on the statistical power and required sample size of the other". “析因临床试验的设计和分析：一种治疗的有效性对另一种治疗的统计能力和所需样本量的影响”的更正。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-09-03 DOI: 10.1177/09622802261471223

引用次数: 0

Combining dependent p-values with transformation using empirical distribution of correlated data and its application for genomic data. 关联数据经验分布的相关p值与变换相结合及其在基因组数据中的应用。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-08-26 DOI: 10.1177/09622802261480383

Junsik Kim, Junyong Park

{"title":"<ArticleTitle xmlns:ns0=\"http://www.w3.org/1998/Math/MathML\">Combining dependent <ns0:math><ns0:mi>p</ns0:mi></ns0:math>-values with transformation using empirical distribution of correlated data and its application for genomic data.","authors":"Junsik Kim, Junyong Park","doi":"10.1177/09622802261480383","DOIUrl":"10.1177/09622802261480383","url":null,"abstract":"Combining dependent <math><mi>p</mi></math>-values is a critical challenge in large-scale hypothesis testing, with applications in genome-wide association studies, transcriptomics, environmental studies, and meta-analyses. Existing methods-such as those based on transforming <math><mi>p</mi></math>-values into heavy-tailed distribution or estimating the correlation matrix of test statistics-often fail to control Type I error under complex dependency structures or rely heavily on impractical assumptions of dependency structures. To address these issues, we develop a new method consisting of two procedures: First, we propose an iterative algorithm to estimate the empirical null distribution function of dependent data. The proposed algorithm incorporates imputations of data simulated from the estimated null distribution. Second, we generate modified <math><mi>p</mi></math>-values based on the estimated empirical null distribution and show that these modified <math><mi>p</mi></math>-values are decorrelated. Combining these modified <math><mi>p</mi></math>-values provides more accurate Type I error control compared to existing methods. In addition, it improves statistical power through the strategy of imputation, while maintaining robustness across various dependency structures. Extensive numerical studies and real-world applications demonstrate the effectiveness of the proposed method in improving both Type I error control and testing power.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2129-2146"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148832389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 calculation for the sequential multiple assignment randomized trial (SMART) design with a skewed outcome: Application to the SMART+ study. 结果偏倚的顺序多重分配随机试验（SMART）设计的样本量计算：在SMART+研究中的应用

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-09-12 DOI: 10.1177/09622802261485814

Arijit Dey, Xiaoxi Yan, Bibhas Chakraborty

{"title":"Sample size calculation for the sequential multiple assignment randomized trial (SMART) design with a skewed outcome: Application to the SMART+ study.","authors":"Arijit Dey, Xiaoxi Yan, Bibhas Chakraborty","doi":"10.1177/09622802261485814","DOIUrl":"10.1177/09622802261485814","url":null,"abstract":"With the rapid emergence of personalized healthcare, adaptive interventions have gained significant traction and relevance. Contemporary research has introduced a sophisticated trial design known as the sequential multiple assignment randomized trial (SMART) to advance the development of effective adaptive interventions. A critical element of the SMART design is the determination of the sample size. The existing literature provides sample size calculation formulas for SMART designs, which encompass a variety of approaches and types of outcome data. However, these formulas have primarily been developed under the assumption of normality for the outcome data. In practice, the fields where SMART is employed exhibit a significant prevalence of non-normality in the outcomes of interest. This paper delves into a specific scenario where the outcome demonstrates skewed behavior. We develop precision-based and power-based formulas for skewed outcomes, considering the requirements of both pilot and full-scale SMARTs. We present the operating characteristics of the formulas and perform extensive simulations under various design specifications to validate their usefulness. To demonstrate practical utility, we apply our formulas to the SMART+ study, a digital intervention trial that includes a skewed outcome among its outcomes of interest, highlighting relevance in real-world planning.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2220-2236"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148956499","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Built-in selection bias in proportional hazards models with omitted covariates: Simulation evidence and alternative approaches. 省略协变量的比例风险模型中的内置选择偏差：模拟证据和替代方法。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-09-12 DOI: 10.1177/09622802261485825

Ayoub Bifenzi, Helene Jacqmin-Gadda

{"title":"Built-in selection bias in proportional hazards models with omitted covariates: Simulation evidence and alternative approaches.","authors":"Ayoub Bifenzi, Helene Jacqmin-Gadda","doi":"10.1177/09622802261485825","DOIUrl":"10.1177/09622802261485825","url":null,"abstract":"In time-to-event analysis, the hazard ratio derived from the Cox proportional hazards model is the most commonly used and widely reported measure for assessing treatment effects. However, hazard ratios are non-collapsible due to their inherent conditioning on survival up to each time point. As a result, they are subject to built-in selection bias in the presence of unmeasured heterogeneity arising from omitted important covariates, even when these covariates are independent of the main exposure at baseline, as is the case in randomized controlled trials. This article aims to provide an overview of key findings from the literature on how unobserved heterogeneity, due to omitted covariates that affect the outcome, can bias the estimation of the treatment hazard ratio in standard proportional hazards models, even in randomized trials where treatment is assigned independently of such covariates. Through simulations, we evaluate the extent of bias in the semi-parametric Cox proportional hazards model and parametric proportional hazards model under various scenarios of unmeasured heterogeneity. We then compare these standard models to alternative approaches that either account for this issue or are considered robust to it. These alternatives include the hazard ratio estimated from frailty models, regression parameters from an accelerated failure time model, and survival differences between treatment groups estimated nonparametrically using Kaplan-Meier curves or based on a Cox model with time-dependent effect of the exposure. We illustrate the practical relevance of the explored alternatives through a real data application to a randomized controlled trial from the Radiation Therapy Oncology Group (RTOG9202).","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2237-2259"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148956536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 general mixture loss function to optimize a personalized predictive model. 一个通用的混合损失函数来优化个性化的预测模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-09-14 DOI: 10.1177/09622802261488055

Tatiana Krikella, Joel A Dubin

{"title":"A general mixture loss function to optimize a personalized predictive model.","authors":"Tatiana Krikella, Joel A Dubin","doi":"10.1177/09622802261488055","DOIUrl":"10.1177/09622802261488055","url":null,"abstract":"Advances in precision medicine increasingly drive methodological innovation in health research. A key development is the use of personalized prediction models (PPMs) for a specific index patient, which are fit using a subpopulation similar to that index patient based on their covariates, and have been shown to outperform one-size-fits-all models, particularly in terms of model discrimination performance. We propose a generalized loss function that enables tuning of the subpopulation size used to fit a PPM. This loss function allows joint optimization of discrimination and calibration, allowing both the performance measures and their relative weights to be specified by the user. To reduce computational burden, we conducted extensive simulation studies to identify practical bounds for the grid of subpopulation sizes. Based on these results, we recommend using a lower bound of 20% and an upper bound of 70% of the entire training dataset. We apply the proposed method to both simulated and real-world datasets and demonstrate that previously observed relationships between subpopulation size and model performance are robust. Furthermore, we show that the choice of performance measures in the loss function influences the optimal subpopulation size selected. These findings support the flexible and computationally efficient implementation of PPMs in precision health research.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2182-2199"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148971399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Inclusive designs that allow the inclusion of a broader population in randomized controlled trials: An evaluation of randomization ratio and analysis strategy. 允许在随机对照试验中纳入更广泛人群的包容性设计：随机化比例和分析策略的评估。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-08-21 DOI: 10.1177/09622802261475687

Kim May Lee, Ziyan Wang, Richard Emsley, Nigel Stallard

{"title":"Inclusive designs that allow the inclusion of a broader population in randomized controlled trials: An evaluation of randomization ratio and analysis strategy.","authors":"Kim May Lee, Ziyan Wang, Richard Emsley, Nigel Stallard","doi":"10.1177/09622802261475687","DOIUrl":"10.1177/09622802261475687","url":null,"abstract":"In most multi-arm randomized controlled trials, all participants need to be eligible to be randomized to all the arms. This means some participants are excluded from participating in multi-arm studies. We propose to relax this by considering a 'differential randomization' approach that allows the inclusion of participants who are eligible for some but not all arms, as in some innovative trial approaches. We refer to a multi-arm design that employs differential randomization as an 'inclusive design' because it maximizes participant inclusion. Considering superiority comparisons for a normal endpoint, we evaluate the performance of some analysis methods for a three-arm inclusive design by simulation studies. Methods include pairwise or overall regression analysis, an averaging approach that pools summary statistics using prevalence rate, and a meta-analysis type framework. We compare the statistical power when the inclusive design employs different treatment allocation schemes. We find that using equal allocation ratio within each subpopulation leads to a higher disjunctive power than using equal allocation across arms, when the many-to-one comparisons are analysed using the corresponding pairwise data separately as opposed to using all data in a single regression analysis. We find that differential covariate-outcome relations among patients can affect the property of the inference.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2095-2115"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148798014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Bayesian adaptive shift-mixture approach for estimating incubation period and exposure time in small outbreaks. 估计小型疫情潜伏期和暴露时间的贝叶斯自适应移位混合方法。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 DOI: 10.1177/09622802261490778

Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi

{"title":"A Bayesian adaptive shift-mixture approach for estimating incubation period and exposure time in small outbreaks.","authors":"Daisuke Yoneoka, Takayuki Kawashima, Yuta Tanoue, Shuhei Nomura, Akifumi Eguchi","doi":"10.1177/09622802261490778","DOIUrl":"https://doi.org/10.1177/09622802261490778","url":null,"abstract":"Rapid estimation of infection time and the incubation-period distribution is essential for evidence-based quarantine and contact tracing. However, outbreak data typically mix primary and higher-generation cases with unobserved exposure times. We propose the infection-induced adaptive shifted-knots Dirichlet process mixture (IASK-DPM) model, a nonparametric Bayesian framework applying a Dirichlet process prior on the shift parameter in a three-parameter lognormal kernel. Incorporating infectious dynamics, the base measure is derived from a kernel-density estimate of observed onset times, concentrating mixture components where the epidemic curve is dense while preserving support in the left tail, where earlier exposure may plausibly lie. The proposed framework is intended primarily for small-outbreak or early-phase epidemic settings in which infections are concentrated around a limited number of dominant transmission episodes or narrow exposure windows. Posterior inference uses Hamiltonian Monte Carlo and is implemented in R/Stan. In nine Monte Carlo scenarios representing COVID-19, measles, and O-157 outbreaks, IASK-DPM reduced mean squared error in shift parameter estimates (i.e. exposure time) by 35% to 70% and in the 95th incubation percentile by 30% to 68%, relative to fixed finite-mixture models. These gains persisted even when the competing Dirichlet-process model used a conventional base measure. Applied to the 2015 Korean MERS outbreak, the method recovered four exposure dates (4.7-19.5 days after May 10, 2015), coinciding with field-investigated superspreading events, and estimated a posterior median incubation of 5.8 days (95th percentile 13.7 days), aligning with exhaustive contact tracing. By allowing the data, rather than the analyst, to determine latent exposure generations, IASK-DPM enables accurate inference on key epidemic parameters with minimal tuning.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261490778"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149115098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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 Bayesian phase I/II trial design to optimizing dose-schedule regimen with competing risk outcomes. 一项贝叶斯I/II期试验设计，以优化具有竞争风险结果的剂量计划方案。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-09-08 DOI: 10.1177/09622802261483484

Wenyun Yang, Bosheng Li, Fangrong Yan

引用次数: 0

Adaptive design strategies for ethical and efficient clinical trials using simulated annealing. 应用模拟退火技术进行伦理和高效临床试验的自适应设计策略。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-10-01 Epub Date: 2026-09-14 DOI: 10.1177/09622802261488058

Rosamarie Frieri, Francesco Mariani, Marco Novelli

{"title":"Adaptive design strategies for ethical and efficient clinical trials using simulated annealing.","authors":"Rosamarie Frieri, Francesco Mariani, Marco Novelli","doi":"10.1177/09622802261488058","DOIUrl":"10.1177/09622802261488058","url":null,"abstract":"We introduce a flexible adaptive design strategy for treatment allocation in clinical trials based on a randomized simulated annealing algorithm. The proposed approach provides a unified and modular framework for implementing a broad range of adaptive randomization objectives, including covariate-adaptive (CA), response-adaptive (RA), covariate-adjusted response-adaptive (CARA), and hybrid RA+CA and CARA+CA designs. The procedure accommodates multiple treatments, different outcome types, and any baseline covariate information available. Its implementation is compatible with both parametric and nonparametric predictive models; in this work, we use Bayesian additive regression trees to capture complex covariate-response relationships without committing to a low-dimensional parametric specification. The allocation rule combines a simulated-annealing-based recommendation with a fallback randomization step used to ensure positive assignment probabilities. In a finite stratified setting, we establish strong consistency of the resulting stratified sample mean estimators. Extensive simulation studies with homogeneous and heterogeneous treatment effects show that our proposal achieves a favorable trade-off between ethical allocation and statistical efficiency. A redesign of the ACTG 175 clinical trial further illustrates the practical advantages and flexibility of the proposed framework.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2200-2219"},"PeriodicalIF":2.4,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148971476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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