Statistical Methods in Medical Research最新文献_第4页

Nonparametric inference for the localization receiver operating characteristic curve and its extension to free-response image localization tasks. 定位接收机工作特性曲线的非参数推断及其在自由响应图像定位任务中的推广。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-07-11 DOI: 10.1177/09622802261468028

Kaiyuan Liu, Xiao-Hua Zhou

引用次数: 0

Nonparametric change-point control charts for joint monitoring of mean and covariance with application to medical imaging data. 联合监测均数和协方差的非参数变点控制图及其在医学影像数据中的应用。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-07-10 DOI: 10.1177/09622802261466468

Guojun Liu, Jyun-You Chiang, Wen Liu, Yi Li

{"title":"Nonparametric change-point control charts for joint monitoring of mean and covariance with application to medical imaging data.","authors":"Guojun Liu, Jyun-You Chiang, Wen Liu, Yi Li","doi":"10.1177/09622802261466468","DOIUrl":"10.1177/09622802261466468","url":null,"abstract":"Monitoring medical imaging data is increasingly important in healthcare because image-derived quantitative features provide valuable information for assessing disease progression, diagnostic quality, and process stability. However, such data are often feature-rich, distributionally complex, and limited in sample size, and existing methods do not always provide a unified way for retrospectively monitoring joint changes in mean and covariance without relying on strong parametric assumptions. Motivated by these challenges, this paper develops a Phase I nonparametric change-point monitoring framework for individual observations. The framework combines existing high-dimensional two-sample statistics for mean and covariance changes into two control chart schemes, whose control limits are determined through simulation. A post-signal diagnostic procedure is also presented to estimate the change-point location, identify whether the detected shift occurs in the mean, the covariance, or both, and screen potentially affected variables. An extensive simulation study considering various distributional models shows that the proposed charts preserve the nominal in-control false alarm probability, achieve favorable out-of-control detection power, and yield informative post-signal diagnostic results. Finally, a real-world colposcopy dataset is used to illustrate the practical applicability of the proposed framework for the retrospective monitoring of medical imaging data.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1959-1979"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148424408","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

Evaluating and testing for actionable treatment effect heterogeneity. 评估和测试可操作的治疗效果异质性。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-07-23 DOI: 10.1177/09622802261470081

Mahsa Ashouri, Nicholas C Henderson

{"title":"Evaluating and testing for actionable treatment effect heterogeneity.","authors":"Mahsa Ashouri, Nicholas C Henderson","doi":"10.1177/09622802261470081","DOIUrl":"10.1177/09622802261470081","url":null,"abstract":"Developing tools for estimating heterogeneous treatment effects (HTEs) and individualized treatment effects has been an area of active research in recent years. While these tools have proven to be useful in many contexts, a concern when deploying such methods is the degree to which incorporating HTE into a prediction model provides an advantage over predictive methods that do not allow for heterogeneity in treatment effect across individuals. To address this concern, we propose a procedure that evaluates the extent to which an HTE model provides a predictive advantage. Specifically, our procedure targets the gain in predictive performance from using a flexible predictive model incorporating HTE versus an alternative partial linear model which is similar to the HTE-utilizing model except that it is constrained to not allow heterogeneity in treatment effect. By drawing upon recent work in using nested cross-validation techniques for prediction error inference, we generate confidence intervals for this measure of gain in predictive performance, which allows one to directly calculate the level at which one is confident of a substantial HTE-modeling gain in prediction-a quantity that we refer to as the predictive HTE <math><mi>p</mi></math>-value. Our procedure is generic and can be directly used to assess the benefit of modeling HTE for any method that incorporates treatment effect heterogeneity.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"2001-2023"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148563025","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 more interpretable regression model for count data with excess of zeros. 一个更可解释的回归模型计数数据与超过零。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-07-11 DOI: 10.1177/09622802261468025

Gustavo H A Pereira, Jeremias Leao, Manoel Santos-Neto, Jianwen Cai

引用次数: 0

Bayesian mediation analysis for zero-inflated mediator. 零膨胀中介的贝叶斯中介分析。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-07-15 DOI: 10.1177/09622802261468022

Jinhong Cui, Xiaoxiao Zhou, Melissa J Smith, Andrew Sims, Justin M Leach, D Leann Long, Nengjun Yi

{"title":"Bayesian mediation analysis for zero-inflated mediator.","authors":"Jinhong Cui, Xiaoxiao Zhou, Melissa J Smith, Andrew Sims, Justin M Leach, D Leann Long, Nengjun Yi","doi":"10.1177/09622802261468022","DOIUrl":"10.1177/09622802261468022","url":null,"abstract":"Mediation analysis is a powerful tool for exploring the causal relationships between exposures and outcomes that are mediated by intermediate variables. In this paper, we propose a flexible Bayesian mediation analysis framework to accommodate zero-inflated mediators, compatible with a wide range of outcome distributions. This novel technique employs Bayesian models for both the mediator and the outcome, utilizing Markov Chain Monte Carlo algorithms for parameter estimation. While addressing the challenges posed by an excess of zeros, we further decompose the mediation effects into components influenced by either the probability of zero or the mean of the non-zero distribution in the mediator. An associated R package mediationBayes (https://github.com/jhcuibst/mediationBayes.git) has been developed to facilitate the application of this framework. Through comprehensive simulation studies, we demonstrate that our method outperforms alternatives in terms of the accuracy of point estimates, coverage probabilities, and the precision of the mediation effects decomposition. We further illustrate the practical applicability of our method by conducting an analysis on the REasons for Geographic And Racial Differences in Stroke Study to investigate the mediating influence of smoking pack-years on the association between educational levels and incident hypertension, where mediation effects are quantified on a risk ratio scale for binary outcomes.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1882-1896"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148449600","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

Improved survival analysis with shrinkage Kibria-Lukman estimators in the Cox model: Application to lung cancer data. 在Cox模型中使用收缩Kibria-Lukman估计器改进生存分析：肺癌数据的应用。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-03-03 DOI: 10.1177/09622802261423186

Solmaz Seifollahi, Mohammad Arashi

引用次数: 0

Estimating the effects of treatment regimes over the course of chronic disease: A multi-state causal framework with baseline confounding. 在慢性疾病过程中评估治疗方案的效果：一个具有基线混淆的多状态因果框架。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-05-19 DOI: 10.1177/09622802261445822

Ming Ding

{"title":"Estimating the effects of treatment regimes over the course of chronic disease: A multi-state causal framework with baseline confounding.","authors":"Ming Ding","doi":"10.1177/09622802261445822","DOIUrl":"10.1177/09622802261445822","url":null,"abstract":"The development of chronic disease is a long-term process involving multiple endpoints. Although multi-state Cox models can estimate state-specific survival risks over time, they are not well suited for comparing the effectiveness of treatment regimes. A discrete-time split-state framework has been proposed, which divides disease states into substates by conditioning on past history. As this framework is both \"memoryless\" and \"memorable,\" the transition rates can be synthesized into summary measures, multimorbidity-adjusted life year (MALY) and substate-specific life year (SSLY). Building on this framework, we propose to investigate the causal effects of static and dynamic treatment regimes over the disease course under the assumptions of constant baseline confounders and instantaneous effects of interventions on transition rates. We identify the optimal treatment regime as the one that maximizes MALY and use SSLY to elucidate the mechanisms of how treatments influence disease progression. In the application, we identified the optimal weight targets in the ARIC study by modeling the disease course in healthy, at-metabolic-risk, coronary heart disease, heart failure, and mortality states. The estimated MALY was 1.80 years higher (95% CI: 0.62, 2.78) under regime \"Normal weight initially and change to overweight if age >65 y\" compared to regime \"Normal weight across all states.\" SSLY decomposition indicates that this gain arises from increased life year in all substates except the healthy state. In summary, our method provides a framework to evaluate the health benefits of treatment regimes over the disease course and has the potential to improve the precision prevention of chronic diseases.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1806-1817"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558798/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147975660","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

What is estimated in cluster randomized crossover trials with informative sizes? A survey of estimands and common estimators. 具有信息量的聚类随机交叉试验的估计值是多少？估算和常用估算器的概览。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-08-08 DOI: 10.1177/09622802261470087

Kenneth M Lee, Andrew B Forbes, Jessica Kasza, Andrew Copas, Brennan C Kahan, Paul J Young, Michael O Harhay, Fan Li

{"title":"What is estimated in cluster randomized crossover trials with informative sizes? A survey of estimands and common estimators.","authors":"Kenneth M Lee, Andrew B Forbes, Jessica Kasza, Andrew Copas, Brennan C Kahan, Paul J Young, Michael O Harhay, Fan Li","doi":"10.1177/09622802261470087","DOIUrl":"10.1177/09622802261470087","url":null,"abstract":"The cluster randomized crossover (CRXO) trial, among other multi-period cluster randomized trial designs, can target average treatment effect (ATE) estimands that equally weigh the contributions of individuals (iATE), clusters (cATE), cluster-periods (cpATE), or periods (pATE). With these weighted ATE estimands, we define different forms of informative sizes, where the treatment effects vary according to cluster, period, and/or cluster-period sizes, causing these estimands to differ. Under such informative sizes, we survey which of the unweighted, inverse cluster-period size weighted, inverse cluster size weighted, and inverse period size weighted: (i) independence estimating equation, (ii) fixed effects model, (iii) exchangeable mixed effects model, and (iv) nested exchangeable mixed effects model treatment effect estimators are consistent for the aforementioned estimands in cross-sectional CRXO designs with continuous outcomes. We demonstrate that with informative sizes, the unweighted and weighted nested exchangeable mixed effects model estimators are not consistent for any meaningful estimand and can yield biased results. In contrast, the unweighted and weighted independence estimating equation, and under specific scenarios, the fixed effects model and exchangeable mixed effects model, can yield consistent and empirically unbiased estimators for meaningful estimands in CRXO trials.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1980-2000"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558815/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697867","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

Stratification in small randomised clinical trials and analysis of covariance: Some simple theory and recommendations. 小型随机临床试验的分层和协方差分析：一些简单的理论和建议。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-08-05 DOI: 10.1177/09622802261454833

Stephen Senn, Franz König, Martin Posch

{"title":"Stratification in small randomised clinical trials and analysis of covariance: Some simple theory and recommendations.","authors":"Stephen Senn, Franz König, Martin Posch","doi":"10.1177/09622802261454833","DOIUrl":"10.1177/09622802261454833","url":null,"abstract":"A simple device for balancing for a continuous covariate in clinical trials is to stratify by whether the covariate is above or below some target value, typically the predicted median. The object is to improve balance of the covariate and hence efficiency of the treatment estimate particularly if the trial is small, as may be the case if the disease in question is rare. This raises an issue as to which model should be used for modelling the effect of treatment on the outcome variable, Y. Should one fit, the stratum indicator, S, the continuous covariate, X, both or neither? When a covariate is added to a linear model there are three consequences for inference: (a) the mean square error effect, (b) the variance inflation factor and (c) second order precision. We consider that it is valuable to consider these three factors separately, even if, ultimately, it is their joint effect that matters. We present some simple theory, concentrating in particular on the variance inflation factor, that may be used to guide trialists in their choice of model. We also consider the case where the precise form of the relationship between the outcome and the covariate is not known. We conclude by recommending that the continuous covariate should always be in the model but that, depending on circumstances, there may be some justification in fitting the stratum indicator also.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1838-1856"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13558804/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148679985","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

A flexible rank-based framework for individualized treatment selection with mixed-type multivariate outcomes. 一个灵活的基于排名的框架，用于混合类型多变量结果的个性化治疗选择。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-07-17 DOI: 10.1177/09622802261454791

Chathura Siriwardhana, Bakeerathan Gunaratnam, Karunarathna Bandara Kulasekera

{"title":"A flexible rank-based framework for individualized treatment selection with mixed-type multivariate outcomes.","authors":"Chathura Siriwardhana, Bakeerathan Gunaratnam, Karunarathna Bandara Kulasekera","doi":"10.1177/09622802261454791","DOIUrl":"10.1177/09622802261454791","url":null,"abstract":"Personalized treatment selection is often based on a single primary endpoint, even though complex diseases are typically monitored using multiple, heterogeneous outcomes. We develop a rank-based framework for individualized treatment selection that accommodates mixed collections of continuous, ordinal, binary, and right-censored survival outcomes. For each outcome and treatment, we summarize the conditional outcome distribution given patient covariates using clinically meaningful parameters, including conditional quantiles, survival probabilities, and tail probabilities at ordered thresholds, and assemble these summaries across treatment arms into vectors with a common structure. Each summary component induces a ranking of the treatments, and discrepancies between outcome-specific rankings and candidate ranking vectors are quantified using a Mahalanobis-type distance that incorporates user-specified outcome weights and an empirically estimated scaling matrix. The optimal treatment is defined as the arm with the most favorable overall rank. Outcome summaries are estimated using flexible tree-based ensemble methods, although other regression or machine-learning tools can be used without altering the decision rule. Monte Carlo simulations across a range of scenarios show reasonably high probabilities of correct treatment selection, with performance improving as the signal-to-noise ratio increases and only mildly affected by the choice of outcome weights. We illustrate the method using data from an AIDS clinical trial, deriving individualized antiretroviral regimen recommendations that balance CD4 response, CD4/CD8 ratio, and a composite time-to-event endpoint.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1818-1837"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148472983","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