Statistical Methods in Medical Research最新文献_第3页

A regularized semiparametric cure-rate model with high-dimensional imaging data. 具有高维成像数据的正则化半参数固化率模型。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-30 DOI: 10.1177/09622802261483498

Jiahui Feng, Muye Nanshan, Haolun Shi, Kin Yau Wong, Kwok Fai Lam, Jiguo Cao

{"title":"A regularized semiparametric cure-rate model with high-dimensional imaging data.","authors":"Jiahui Feng, Muye Nanshan, Haolun Shi, Kin Yau Wong, Kwok Fai Lam, Jiguo Cao","doi":"10.1177/09622802261483498","DOIUrl":"https://doi.org/10.1177/09622802261483498","url":null,"abstract":"Structural magnetic resonance imaging (MRI) is informative for studying progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD), but incorporating high-dimensional images into survival models requires methods that accommodate irregular brain domains and yield interpretable spatial effects. We propose a regularized semiparametric mixture cure-rate model for high-dimensional imaging predictors that separates susceptibility to AD conversion from time to AD among susceptible subjects. Baseline MRI images are represented by bivariate Bernstein spline basis functions over a triangulation of the brain domain, and sparsity-inducing penalties are imposed on imaging effects in both incidence and latency components. This construction enables direct estimation of localized coefficient functions describing how image regions are associated with susceptibility and conversion time. We develop an expectation-maximization algorithm that updates latent susceptibility probabilities and solves penalized logistic and Cox regression subproblems, together with an efficient tuning strategy. Simulations show that the method can recover localized active regions and provide useful incidence and latency discrimination. An application to Alzheimer's Disease Neuroimaging Initiative data demonstrates susceptibility discrimination and interpretable brain-region coefficient maps.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261483498"},"PeriodicalIF":2.4,"publicationDate":"2026-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149081894","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 new bivariate cure rate model and its application to diabetic retinopathy. 一种新的二元治愈率模型及其在糖尿病视网膜病变中的应用。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-29 DOI: 10.1177/09622802261489815

Matilda Pitt, Katherine Davies

引用次数: 0

Editorial for the special collection "response-adaptive randomization in clinical trials". “临床试验中的反应适应性随机化”特刊社论。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-29 DOI: 10.1177/09622802261486806

David S Robertson, Oleksandr Sverdlov, Sofía S Villar, Hongjian Zhu

引用次数: 0

Generalizing the two sources capture-recapture estimator for covariate information. 推广了协变量信息的两个源捕获-再捕获估计器。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-29 DOI: 10.1177/09622802261491945

Patarawan Sangnawakij, Rattana Lerdsuwansri, Parawan Pijitrattana, Peter Schlattmann, Antonello Maruotti, Dankmar Böhning

{"title":"Generalizing the two sources capture-recapture estimator for covariate information.","authors":"Patarawan Sangnawakij, Rattana Lerdsuwansri, Parawan Pijitrattana, Peter Schlattmann, Antonello Maruotti, Dankmar Böhning","doi":"10.1177/09622802261491945","DOIUrl":"https://doi.org/10.1177/09622802261491945","url":null,"abstract":"Dual system estimation is a statistical tool used to estimate the hidden population where each of the two sources covers part of the population. In estimation, the widely used Lincoln-Petersen and Chapman estimators are used under the assumption of two-source independence. However, this situation is often unrealistic, and the traditional estimators might have a large bias, especially if the two sources experience a large dependence. To improve the methodology, covariate information is used for stratification. We formulate the strata-adjusted Chapman estimator for estimating the population size. Furthermore, the log-linear modeling approach is adopted to cope with covariate information. Two semi-parametric (imputed) bootstrap methods using the log-linear model with and without incorporating the model selection procedure are proposed to quantify the uncertainty surrounding the estimates of the unknown population size. Across all designs, methods that exploit covariate information-either via stratification or within the log-linear modeling framework-consistently reduce bias and mean squared error relative to unadjusted estimators that ignore heterogeneity. For interval estimation, the imputed bootstrap that incorporates model selection in each replicate achieves coverage close to the nominal level across heterogeneous and sparse regimes, typically outperforming both the fixed-model bootstrap and normal-theory intervals for the strata-adjusted Chapman. We illustrate these approaches in an application to estimate the hidden number of suicides in Cambodia and heroin users in Thailand.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261491945"},"PeriodicalIF":2.4,"publicationDate":"2026-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149075896","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

Doubly robust estimation of causal survival effects via inverse probability of treatment and censoring weighting and semi-parametric AFT models with variable selection. 通过反概率治疗和筛选加权和具有变量选择的半参数AFT模型对因果生存效应进行双稳健估计。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-19 DOI: 10.1177/09622802261488051

Chien-Lin Su

{"title":"Doubly robust estimation of causal survival effects via inverse probability of treatment and censoring weighting and semi-parametric AFT models with variable selection.","authors":"Chien-Lin Su","doi":"10.1177/09622802261488051","DOIUrl":"https://doi.org/10.1177/09622802261488051","url":null,"abstract":"Clinical trials and observational studies frequently encounter survival data with right censoring and high-dimensional confounders. This article proposes several estimators for assessing the average causal effect of a binary treatment on survival outcomes in the presence of confounders. These include doubly weighted estimators, regression model-based estimators, and doubly robust estimators. The proposed doubly robust estimators are shown to be consistent when either the treatment model or the semiparametric regression model for survival outcomes is correctly specified. A variable selection technique based on an adaptive sparse group lasso regularization approach is proposed to identify important confounders among high-dimensional covariates for both treatment and outcome models. This method enables simultaneous variable selection and regularization for both models. The performance of the proposed methodologies is thoroughly evaluated through simulation studies. Finally, the practical utility of the proposed methods is illustrated through applications to two real-world datasets.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802261488051"},"PeriodicalIF":2.4,"publicationDate":"2026-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149008905","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

Advancing multilevel Bayesian networks with efficient Bayesian inference. 基于高效贝叶斯推理的多级贝叶斯网络。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-09-10 DOI: 10.1177/09622802261468020

Bezalem Eshetu Yirdaw, Legesse Kassa Debusho, Janet van Niekerk, Håvard Rue

{"title":"Advancing multilevel Bayesian networks with efficient Bayesian inference.","authors":"Bezalem Eshetu Yirdaw, Legesse Kassa Debusho, Janet van Niekerk, Håvard Rue","doi":"10.1177/09622802261468020","DOIUrl":"10.1177/09622802261468020","url":null,"abstract":"Bayesian networks (BNs) are widely used tools for the modeling of complex dependencies among variables. However, their application to multilevel or clustered data remains constrained, especially in scenarios involving multiple random effects, due to a lack of methods and more pressing, efficient computational frameworks. In this article, we propose an innovative framework, INLA-MBN, that integrates multilevel BNs (MBNs) with the integrated nested Laplace approximation (INLA), thereby facilitating efficient structure and parameter learning in both longitudinal and cross-sectional multilevel data contexts. To the best of our knowledge, this is the first implementation of INLA for BNs that involves more than one correlated random effect. More precisely, we advance existing research by modeling longitudinal data with two correlated random effects and multilevel cross-sectional data with more than two correlated random effects. Our approach encompasses both Gaussian and hybrid MBNs that incorporate Gaussian and categorical nodes. Through an exhaustive simulation study, we demonstrate that INLA-MBN exhibit superior performance compared to networks based on maximum likelihood estimation, particularly, in scenarios characterized by small group sizes or limited repeated measurements per subject. INLA-MBN achieves a fully inferred MBN, while negating the cost associated with MBNs based on Markov Chain Monte Carlo. The proposed method was also applied to real-life longitudinal child morbidity data to demonstrate its practical applicability.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"35 9","pages":"1910-1931"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13562775/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148920257","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 bivariate cure model with shared random effect and associated inference and application to diabetic retinopathy data. 一个灵活的双变量治疗模型，具有共享随机效应和相关推断，并应用于糖尿病视网膜病变数据。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-07-30 DOI: 10.1177/09622802261468019

Saptangshu Nandi, Sandip Barui, Debanjan Mitra, Narayanaswamy Balakrishnan

{"title":"A flexible bivariate cure model with shared random effect and associated inference and application to diabetic retinopathy data.","authors":"Saptangshu Nandi, Sandip Barui, Debanjan Mitra, Narayanaswamy Balakrishnan","doi":"10.1177/09622802261468019","DOIUrl":"10.1177/09622802261468019","url":null,"abstract":"In lifetime studies, a proportion of subjects may never experience the event of interest, giving rise to the notion of cure rate. While traditional cure models address this in univariate set-up, many applications involve paired lifetimes, such as twin survival studies, paired organs, or dependent components in a system. In such cases, both the possibility of cure in one or both marginals and the dependence between lifetimes need to be modeled simultaneously, motivating bivariate cure models. We propose here a flexible framework for analyzing bivariate cure data by combining parametric modeling of each marginal survival distribution with a shared frailty term for capturing dependence. The shared frailty is assumed to follow a generalized gamma distribution, providing substantial flexibility in representing diverse dependence structures induced by unobserved frailty. This formulation accommodates cured individuals in one or both marginals and captures the joint survival dynamics of susceptible pairs. Likelihood-based inference for the proposed model is then developed, with estimation carried out using the expectation-maximization algorithm. The performance of the model and the associated inferential method are then evaluated through extensive simulation studies, demonstrating the efficiency and accuracy in estimating model parameters. A real data application, concerning diabetic retinopathy, further illustrates how the model provides insights into cure dynamics and associations that will not be evident in univariate analyses. The proposed bivariate cure model offers a comprehensive statistical tool for jointly studying cure fractions and survival dependence in paired lifetime settings, broadening the applicability of cure rate methodology in biomedical research.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1857-1881"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148621283","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

Covariate-adaptive randomization with dropouts. 带退出的协变量自适应随机化。

IF 2.4 3区医学

Statistical Methods in Medical Research Pub Date : 2026-09-01 Epub Date: 2026-09-10 DOI: 10.1177/09622802261475671

Meng-Hsuan Sung, Qian Xiao, Ye Shen

{"title":"Covariate-adaptive randomization with dropouts.","authors":"Meng-Hsuan Sung, Qian Xiao, Ye Shen","doi":"10.1177/09622802261475671","DOIUrl":"https://doi.org/10.1177/09622802261475671","url":null,"abstract":"While clinical trials are often regarded as the gold standard by medical researchers and regulatory agencies, the statistical properties of typical randomized controlled trials heavily depend on large sample sizes, which may not always be achievable due to budget constraints or other practical limitations. To address this issue, adaptive designs have gained increasing attention since the 1970s. These designs incorporate modifications in trial procedures or statistical methodologies, aiming to fulfill study objectives more efficiently using smaller sample sizes and shorter durations compared to traditional clinical trials. Specifically, covariate-adaptive designs are developed to reduce the risk of significant covariate imbalances among treatment groups, particularly in studies with limited sample sizes. Additionally, patient dropout during trials significantly impacts statistical inference regarding treatment effects and diminishes statistical power. Ignoring dropout events can result in undesirable participant allocation and substantial treatment imbalances. In this article, we propose novel strategies that explicitly incorporate dropout information into covariate-adaptive randomization. We thoroughly investigate the statistical properties of covariate-adaptive designs adjusted for dropouts. Extensive simulation studies highlight the advantages of the proposed approaches. Our research lays a robust foundation for future developments in covariate-adaptive randomization, specifically addressing dropout challenges.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":"35 9","pages":"2024-2044"},"PeriodicalIF":2.4,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148920326","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

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