Statistical Methods in Medical Research最新文献_第7页

Permutation-based global rank test with adaptive weights for multiple primary endpoints. 基于置换的多主端点自适应权重全局秩检验。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-05-14 DOI: 10.1177/09622802251334886

Satoshi Yoshida, Yusuke Yamaguchi, Kazushi Maruo, Masahiko Gosho

{"title":"Permutation-based global rank test with adaptive weights for multiple primary endpoints.","authors":"Satoshi Yoshida, Yusuke Yamaguchi, Kazushi Maruo, Masahiko Gosho","doi":"10.1177/09622802251334886","DOIUrl":"10.1177/09622802251334886","url":null,"abstract":"Multiple efficacy endpoints are investigated in clinical trials, and selecting the appropriate primary endpoints is key to the study's success. The global test is an analysis approach that can handle multiple endpoints without multiplicity adjustment. This test, which aggregates the statistics from multiple primary endpoints into a single statistic using weights for the statistical comparison, has been gaining increasing attention. A key consideration in the global test is determination of the weights. In this study, we propose a novel global rank test in which the weights for each endpoint are estimated based on the current study data to maximize the test statistic, and the permutation test is applied to control the type I error rate. Simulation studies conducted to compare the proposed test with other global tests show that the proposed test can control the type I error rate at the nominal level, regardless of the number of primary endpoints and correlations between endpoints. Additionally, the proposed test offers higher statistical powers when the efficacy is considerably different between endpoints or when endpoints are moderately correlated, such as when the correlation coefficient is greater than or equal to 0.5.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1254-1266"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080760","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

Rank-based estimators of global treatment effects for cluster randomized trials with multiple endpoints on different scales. 在不同尺度上具有多个终点的聚类随机试验的总体治疗效果的基于秩的估计。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-05-14 DOI: 10.1177/09622802251338387

Emma Davies Smith, Vipul Jairath, Guangyong Zou

{"title":"Rank-based estimators of global treatment effects for cluster randomized trials with multiple endpoints on different scales.","authors":"Emma Davies Smith, Vipul Jairath, Guangyong Zou","doi":"10.1177/09622802251338387","DOIUrl":"10.1177/09622802251338387","url":null,"abstract":"Cluster randomized trials commonly employ multiple endpoints. When a single summary of treatment effects across endpoints is of primary interest, global methods represent a common analysis strategy. However, specification of the required joint distribution is non-trivial, particularly when the endpoints have different scales. We develop rank-based interval estimators for a global treatment effect referred to here as the \"global win probability, or the mean of multiple Wilcoxon Mann-Whitney probabilities, and interpreted as the probability that a treatment individual responds better than a control individual on average. Using endpoint-specific ranks among the combined sample and within each arm, each individual-level observation is converted to a \"win fraction\" which quantifies the proportion of wins experienced over every observation in the comparison arm. An individual's multiple observations are then replaced with a single \"global win fraction\" by averaging win fractions across endpoints. A linear mixed model is applied directly to the global win fractions to obtain point, variance, and interval estimates adjusted for clustering. Simulation demonstrates our approach performs well concerning confidence interval coverage and type I error, and methods are easily implemented using standard software. A case study using public data is provided with corresponding R and SAS code.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1267-1289"},"PeriodicalIF":1.9,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12478089/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080761","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

G-formula with multiple imputation for causal inference with incomplete data. 不完全数据下的多重归因g公式。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802251316971

Jonathan W Bartlett, Camila Olarte Parra, Emily Granger, Ruth H Keogh, Erik W van Zwet, Rhian M Daniel

引用次数: 0

Optimising error rates in programmes of pilot and definitive trials using Bayesian statistical decision theory. 利用贝叶斯统计决策理论优化试点和最终试验方案的错误率。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802251322987

Duncan T Wilson, Andrew Hall, Julia M Brown, Rebecca Ea Walwyn

{"title":"Optimising error rates in programmes of pilot and definitive trials using Bayesian statistical decision theory.","authors":"Duncan T Wilson, Andrew Hall, Julia M Brown, Rebecca Ea Walwyn","doi":"10.1177/09622802251322987","DOIUrl":"10.1177/09622802251322987","url":null,"abstract":"Pilot trials are often conducted in advance of definitive trials to assess their feasibility and to inform their design. Although pilot trials typically collect primary endpoint data, preliminary tests of effectiveness have been discouraged given their typically low power. Power could be increased at the cost of a higher type I error rate, but there is little methodological guidance on how to determine the optimal balance between these operating characteristics. We consider a Bayesian decision-theoretic approach to this problem, introducing a utility function and defining an optimal pilot and definitive trial programme as that which maximises expected utility. We base utility on changes in average primary outcome, the cost of sampling, treatment costs, and the decision-maker's attitude to risk. We apply this approach to re-design OK-Diabetes, a pilot trial of a complex intervention with a continuous primary outcome with known standard deviation. We then examine how optimal programme characteristics vary with the parameters of the utility function. We find that the conventional approach of not testing for effectiveness in pilot trials can be considerably sub-optimal.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1162-1177"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209550/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754028","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

An iterative matrix uncertainty selector for high-dimensional generalized linear models with measurement errors. 具有测量误差的高维广义线性模型的迭代矩阵不确定性选择器。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-19 DOI: 10.1177/09622802251316963

Betrand Fesuh Nono, Georges Nguefack-Tsague, Martin Kegnenlezom, Eugène-Patrice N Nguéma

{"title":"An iterative matrix uncertainty selector for high-dimensional generalized linear models with measurement errors.","authors":"Betrand Fesuh Nono, Georges Nguefack-Tsague, Martin Kegnenlezom, Eugène-Patrice N Nguéma","doi":"10.1177/09622802251316963","DOIUrl":"10.1177/09622802251316963","url":null,"abstract":"Measurement error is a prevalent issue in high-dimensional generalized linear regression that existing regularization techniques may inadequately address. Most require estimating error distributions, which can be computationally prohibitive or unrealistic. We introduce an error distribution-free approach for variable selection called the Iterative Matrix Uncertainty Selector (IMUS). IMUS employs the matrix uncertainty selector framework for linear models, which is known for its selection consistency properties. It features an efficient iterative algorithm easily implemented for any generalized linear model within the exponential family. Empirically, we demonstrate that IMUS performs well in simulations and on three microarray gene expression datasets, achieving effective covariate selection with smoother convergence and clearer elbow criteria compared to other error distribution free methods. Notably, simulation studies in logistic and Poisson regression showed that IMUS exhibited smoother convergence and clearer elbow criteria, performing comparably to the Generalized Matrix Uncertainty Selector (GMUS) and Generalized Matrix Uncertainty Lasso (GMUL) in covariate selection. In many scenarios, IMUS had smaller estimation errors than GMUL and GMUS, measured by both the 1- and 2-norms. In applications to three microarray datasets with noisy measurements, GMUS faced convergence issues, while GMUL converged but lacked well-defined elbows for two datasets. In contrast, IMUS converged with well-defined elbows for all datasets, providing a potentially effective solution for high dimensional regression problems involving measurement errors.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1114-1129"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664493","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

Sequential design for paired ordinal categorical outcome. 配对有序分类结果的序贯设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802251322990

Baoshan Zhang, Yuan Wu

引用次数: 0

Estimand-based inference in the presence of long-term survivors. 在长期幸存者面前的基于估计的推断。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802251327686

Yi-Cheng Tai, Weijing Wang, Martin T Wells

{"title":"Estimand-based inference in the presence of long-term survivors.","authors":"Yi-Cheng Tai, Weijing Wang, Martin T Wells","doi":"10.1177/09622802251327686","DOIUrl":"10.1177/09622802251327686","url":null,"abstract":"In this article, we develop nonparametric inference methods for comparing survival data across two samples, beneficial for clinical trials of novel cancer therapies where long-term survival is critical. These therapies, including immunotherapies and other advanced treatments, aim to establish durable effects. They often exhibit distinct survival patterns such as crossing or delayed separation and potentially leveling-off at the tails of survival curves, violating the proportional hazards assumption and rendering the hazard ratio inappropriate for measuring treatment effects. Our methodology uses the mixture cure framework to separately analyze cure rates of long-term survivors and the survival functions of susceptible individuals. We evaluated a nonparametric estimator for the susceptible survival function in a one-sample setting. Under sufficient follow-up, it is expressed as a location-scale-shift variant of the Kaplan-Meier estimator. It retains desirable features of the Kaplan-Meier estimator, including inverse-probability-censoring weighting, product-limit estimation, self-consistency, and nonparametric efficiency. Under insufficient follow-up, it can be adapted by incorporating a suitable cure rate estimator. In the two-sample setting, in addition to using the difference in cure rates to measure long-term effects, we propose a graphical estimand to compare relative treatment effects on susceptible subgroups. This process, inspired by Kendall's tau, compares the order of survival times among susceptible individuals. Large-sample properties of the proposed methods are derived for inference and their finite-sample properties are evaluated through simulations. The methodology is applied to analyze digitized data from the CheckMate 067 trial.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1178-1191"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143753827","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 computationally efficient approach to false discovery rate control and power maximisation via randomisation and mirror statistic. 一种通过随机化和镜像统计实现错误发现率控制和功率最大化的高效计算方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802251329768

Marco Molinari, Magne Thoresen

{"title":"A computationally efficient approach to false discovery rate control and power maximisation via randomisation and mirror statistic.","authors":"Marco Molinari, Magne Thoresen","doi":"10.1177/09622802251329768","DOIUrl":"10.1177/09622802251329768","url":null,"abstract":"Simultaneously performing variable selection and inference in high-dimensional regression models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of specific statistical procedures to accurately select the most important predictors in a high-dimensional space, while controlling the false discovery rate (FDR) associated with the variable selection procedure. In this paper, we propose the joint adoption of the Mirror Statistic approach to FDR control, coupled with outcome randomisation to maximise the statistical power of the variable selection procedure, measured through the true positive rate. Through extensive simulations, we show how our proposed strategy allows us to combine the benefits of the two techniques. The Mirror Statistic is a flexible method to control FDR, which only requires mild model assumptions, but requires two sets of independent regression coefficient estimates, usually obtained after splitting the original dataset. Outcome randomisation is an alternative to data splitting that allows to generate two independent outcomes, which can then be used to estimate the coefficients that go into the construction of the Mirror Statistic. The combination of these two approaches provides increased testing power in a number of scenarios, such as highly correlated covariates and high percentages of active variables. Moreover, it is scalable to very high-dimensional problems, since the algorithm has a low memory footprint and only requires a single run on the full dataset, as opposed to iterative alternatives such as multiple data splitting.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1233-1253"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209545/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754551","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

Dose selection criteria to identify the optimal dose based on ranked efficacy-toxicity outcomes without reliance on clinical utilities. 剂量选择标准，根据分级的疗效-毒性结果确定最佳剂量，而不依赖于临床效用。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802251327691

Sydney Porter, Anne Eaton, Thomas A Murray

{"title":"Dose selection criteria to identify the optimal dose based on ranked efficacy-toxicity outcomes without reliance on clinical utilities.","authors":"Sydney Porter, Anne Eaton, Thomas A Murray","doi":"10.1177/09622802251327691","DOIUrl":"10.1177/09622802251327691","url":null,"abstract":"Recently, targeted and immunotherapy cancer treatments have motivated dose-finding based on efficacy-toxicity trade-offs rather than toxicity alone. The EffTox and utility-based Bayesian optimal interval (U-BOIN) dose-finding designs were developed in response to this need, but may be sensitive to elicited subjective design parameters that reflect the trade-off between efficacy and toxicity. To ease elicitation and reduce subjectivity, we propose dose desirability criteria that only depend on a preferential ordering of the joint efficacy-toxicity outcomes. We propose two novel order-based criteria and compare them with utility-based and contour-based criteria when paired with the design framework and probability models of EffTox and U-BOIN. The proposed dose desirability criteria simplify implementation and improve robustness to the elicited subjective design parameters and perform similarly in simulation studies to the established EffTox and U-BOIN designs when the ordering of the joint outcomes is equivalent. We also propose an alternative dose admissibility criteria based on the joint efficacy and toxicity profile of a dose rather than its marginal toxicity and efficacy profile. We argue that this alternative joint criterion is more consistent with defining dose desirability in terms of efficacy-toxicity trade-offs than the standard marginal admissibility criteria. The proposed methods enhance the usability and robustness of dose-finding designs that account for efficacy-toxicity trade-offs to identify the optimal biological dose.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1219-1232"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754568","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 Weibull mixture cure frailty model for high-dimensional covariates. 高维协变量的Weibull混合治疗脆弱性模型。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-06-01 Epub Date: 2025-03-31 DOI: 10.1177/09622802251327687

Fatih Kızılaslan, David Michael Swanson, Valeria Vitelli

{"title":"A Weibull mixture cure frailty model for high-dimensional covariates.","authors":"Fatih Kızılaslan, David Michael Swanson, Valeria Vitelli","doi":"10.1177/09622802251327687","DOIUrl":"10.1177/09622802251327687","url":null,"abstract":"A novel mixture cure frailty model is introduced for handling censored survival data. Mixture cure models are preferable when the existence of a cured fraction among patients can be assumed. However, such models are heavily underexplored: frailty structures within cure models remain largely undeveloped, and furthermore, most existing methods do not work for high-dimensional datasets, when the number of predictors is significantly larger than the number of observations. In this study, we introduce a novel extension of the Weibull mixture cure model that incorporates a frailty component, employed to model an underlying latent population heterogeneity with respect to the outcome risk. Additionally, high-dimensional covariates are integrated into both the cure rate and survival part of the model, providing a comprehensive approach to employ the model in the context of high-dimensional omics data. We also perform variable selection via an adaptive elastic-net penalization, and propose a novel approach to inference using the expectation-maximization (EM) algorithm. Extensive simulation studies are conducted across various scenarios to demonstrate the performance of the model, and results indicate that our proposed method outperforms competitor models. We apply the novel approach to analyze RNAseq gene expression data from bulk breast cancer patients included in The Cancer Genome Atlas (TCGA) database. A set of prognostic biomarkers is then derived from selected genes, and subsequently validated via both functional enrichment analysis and comparison to the existing biological literature. Finally, a prognostic risk score index based on the identified biomarkers is proposed and validated by exploring the patients' survival.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1192-1218"},"PeriodicalIF":1.6,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12209551/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754552","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