Statistical Methods in Medical Research最新文献_第5页

Model-based approach for two-stage group sequential or adaptive designs in bioequivalence studies using parallel and crossover designs. 采用平行和交叉设计的生物等效性研究中两阶段组序贯或自适应设计的基于模型的方法。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-13 DOI: 10.1177/09622802251354925

Florence Loingeville, Manel Rakez, Thu Thuy Nguyen, Mark Donnelly, Lanyan Fang, Kevin Feng, Liang Zhao, Stella Grosser, Guoying Sun, Wanjie Sun, France Mentré, Julie Bertrand

{"title":"Model-based approach for two-stage group sequential or adaptive designs in bioequivalence studies using parallel and crossover designs.","authors":"Florence Loingeville, Manel Rakez, Thu Thuy Nguyen, Mark Donnelly, Lanyan Fang, Kevin Feng, Liang Zhao, Stella Grosser, Guoying Sun, Wanjie Sun, France Mentré, Julie Bertrand","doi":"10.1177/09622802251354925","DOIUrl":"https://doi.org/10.1177/09622802251354925","url":null,"abstract":"In pharmacokinetic (PK) bioequivalence (BE) analysis, the recommended approach is the two one-sided tests (TOSTs) on non-compartmental analysis (NCA) estimates of area under the plasma drug concentration versus time curve and <math><msub><mi>C</mi><mrow><mi>m</mi><mi>a</mi><mi>x</mi></mrow></msub></math> (NCA-TOST). Sample size estimation for a BE study requires assumptions on between/within subject variability (B/WSV). When little prior information is available, interim analysis using two-stage group sequential (GS) or adaptive designs (ADs) may be beneficial. GS fixes the second stage size, while AD requires sample re-estimation based on first-stage results. Recent research has proposed model-based (MB) TOST, using nonlinear mixed effects models, as an alternative to NCA-TOST. This work extends GS and AD approaches to MB-TOST. We evaluated these approaches on simulated parallel and two-way crossover designs for a one-compartment PK model, considering three variability levels for initial sample size calculation. We compared final sample size, type I error, and power estimates from one-stage, GS, and AD designs using NCA-TOST and MB-TOST. Results showed both NCA-TOST and MB-TOST reasonably controlled type I error while maintaining adequate power in two-stage GS and AD approaches, based on our limited computation power. Two-stage designs reduced sample size compared to traditional designs, especially for highly variable drugs, with many trials stopping at Stage 1 in AD designs. Our findings suggest MB-TOST may serve as a viable alternative to NCA-TOST for BE assessment in two-stage designs, especially when B/WSV impacts BE results.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251354925"},"PeriodicalIF":1.6,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144627016","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

Estimation of receiver operating characteristic curve when case and control require different transformations for normality. 病例与对照需要不同正态变换时的受试者工作特性曲线估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-09 DOI: 10.1177/09622802251354921

Xiaoyu Cai, Wei Zhang, Huiyun Li, Zhaohai Li, Aiyi Liu

{"title":"Estimation of receiver operating characteristic curve when case and control require different transformations for normality.","authors":"Xiaoyu Cai, Wei Zhang, Huiyun Li, Zhaohai Li, Aiyi Liu","doi":"10.1177/09622802251354921","DOIUrl":"https://doi.org/10.1177/09622802251354921","url":null,"abstract":"The receiver operating characteristic curve is a popular tool for evaluating the discriminative ability of a diagnostic biomarker. Parametric and nonparametric methods exist in the literature for estimation of a receiver operating characteristic curve and its associated summary measures using data usually collected from a case-control study. Since the receiver operating characteristic curve remains unchanged under a monotone transformation, the biomarker data from both cases (diseased subjects) and controls (non-diseased subjects) are often transformed based on a common Box-Cox transformation (or other appropriate transformation) prior to the application of a parametric estimation method. However, careful examination of the data often reveals that the biomarker values in the diseased and non-diseased population can only be normally approximated via different transformations. In this situation, existing estimation methods cannot be directly applied to the heterogeneously-transformed data. In this article, we deal with the situation that biomarker data from both diseased and non-diseased population are normally distributed after being transformed with different Box-Cox transformations. Under this assumption, we show that existing methods based on a common Box-Cox transformation are invalid in that they possess substantial biases. We move on to propose a method to estimate the underlying receiver operating characteristic curve and its area under the curve, and investigate its performance as compared to the nonparametric estimator that ignores any distributional assumptions as well as the estimators based on a common Box-Cox transformation assumptions. The method is exemplified with HIV infection data from the National Health and Nutrition Examination Survey (NHANES).","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251354921"},"PeriodicalIF":1.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592388","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

Inference procedures in sequential trial emulation with survival outcomes: Comparing confidence intervals based on the sandwich variance estimator, bootstrap and jackknife. 具有生存结果的序贯试验模拟中的推理程序：比较基于三明治方差估计器、bootstrap和jackknife的置信区间。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-09 DOI: 10.1177/09622802251356594

Juliette M Limozin, Shaun R Seaman, Li Su

{"title":"Inference procedures in sequential trial emulation with survival outcomes: Comparing confidence intervals based on the sandwich variance estimator, bootstrap and jackknife.","authors":"Juliette M Limozin, Shaun R Seaman, Li Su","doi":"10.1177/09622802251356594","DOIUrl":"https://doi.org/10.1177/09622802251356594","url":null,"abstract":"Sequential trial emulation (STE) is an approach to estimating causal treatment effects by emulating a sequence of target trials from observational data. In STE, inverse probability weighting is commonly utilised to address time-varying confounding and/or dependent censoring. Then structural models for potential outcomes are applied to the weighted data to estimate treatment effects. For inference, the simple sandwich variance estimator is popular but conservative, while nonparametric bootstrap is computationally expensive, and a more efficient alternative, linearised estimating function (LEF) bootstrap, has not been adapted to STE. We evaluated the performance of various methods for constructing confidence intervals (CIs) of marginal risk differences in STE with survival outcomes by comparing the coverage of CIs based on nonparametric/LEF bootstrap, jackknife, and the sandwich variance estimator through simulations. LEF bootstrap CIs demonstrated better coverage than nonparametric bootstrap CIs and sandwich-variance-estimator-based CIs with small/moderate sample sizes, low event rates and low treatment prevalence, which were the motivating scenarios for STE. They were less affected by treatment group imbalance and faster to compute than nonparametric bootstrap CIs. With large sample sizes and medium/high event rates, the sandwich-variance-estimator-based CIs had the best coverage and were the fastest to compute. These findings offer guidance in constructing CIs in causal survival analysis using STE.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251356594"},"PeriodicalIF":1.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592390","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

Health utility adjusted survival: A composite endpoint for clinical trial designs. 健康效用调整生存率：临床试验设计的复合终点。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-09 DOI: 10.1177/09622802251338409

Yangqing Deng, John de Almeida, Wei Xu

{"title":"Health utility adjusted survival: A composite endpoint for clinical trial designs.","authors":"Yangqing Deng, John de Almeida, Wei Xu","doi":"10.1177/09622802251338409","DOIUrl":"https://doi.org/10.1177/09622802251338409","url":null,"abstract":"Many randomized trials have used overall survival as the primary endpoint for establishing non-inferiority of one treatment compared to another. However, if a treatment is non-inferior to another treatment in terms of overall survival, clinicians may be interested in further exploring which treatment results in better health utility scores for patients. Examining health utility in a secondary analysis is feasible, however, since health utility is not the primary endpoint, it is usually not considered in the sample size calculation, hence the power to detect a difference of health utility is not guaranteed. Furthermore, often the premise of non-inferiority trials is to test the assumption that an intervention provides superior quality of life or toxicity profile without compromising survival when compared to the existing standard. Based on this consideration, it may be beneficial to consider both survival and utility when designing a trial. There have been methods that can combine survival and quality of life into a single measure, but they either have strong restrictions or lack theoretical frameworks. In this manuscript, we propose a method called health utility adjusted survival, which can combine survival outcome and longitudinal utility measures for treatment comparison. We propose an innovative statistical framework as well as procedures to conduct power analysis and sample size calculation. By comprehensive simulation studies involving summary statistics from the PET-NECK trial, we demonstrate that our new approach can achieve superior power performance using relatively small sample sizes, and our composite endpoint can be considered as an alternative to overall survival in future clinical trial design and analysis where both survival and health utility are of interest.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251338409"},"PeriodicalIF":1.6,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144592389","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

Reproducible feature selection in heterogeneous multicenter datasets via sign-consistency criteria. 基于符号一致性标准的异构多中心数据集可重复特征选择。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-01 Epub Date: 2025-05-14 DOI: 10.1177/09622802251338375

Xun Zhao, Yalu Ping

{"title":"Reproducible feature selection in heterogeneous multicenter datasets via sign-consistency criteria.","authors":"Xun Zhao, Yalu Ping","doi":"10.1177/09622802251338375","DOIUrl":"10.1177/09622802251338375","url":null,"abstract":"The identification of risk features associated with disease plays a crucial role in biomedical fields. These features are often used to provide evidence for clinical decision-making. However, in the presence of between-center heterogeneity, covariate effects across data centers may exhibit inconsistent directions, making feature selection challenging. In this work, we propose a novel framework to select reproducible risk features whose underlying effects are consistent across different centers. We quantify the feature reproducibility based on the sign-consistency criterion, which provides an acceptable level of heterogeneity in effect sizes and ensures the reasonable similarity of reproducible signals. Compared with the existing feature selection methods, our proposed method effectively protects data privacy and does not rely on the assumption of data homogeneity. Extensive simulations demonstrated that the proposed method has greater power than existing methods do. We apply the proposed approach to analyze data from the China Health and Retirement Study Longitudinal Study (CHARLS) and identify nine important risk factors that show reproducible associations with depression.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1328-1341"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080762","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

Fast leave-one-cluster-out cross-validation using clustered network information criterion. 基于聚类网络信息准则的快速留一簇出交叉验证。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-01 Epub Date: 2025-06-19 DOI: 10.1177/09622802251345486

Jiaxing Qiu, Douglas E Lake, Pavel Chernyavskiy, Teague R Henry

{"title":"Fast leave-one-cluster-out cross-validation using clustered network information criterion.","authors":"Jiaxing Qiu, Douglas E Lake, Pavel Chernyavskiy, Teague R Henry","doi":"10.1177/09622802251345486","DOIUrl":"10.1177/09622802251345486","url":null,"abstract":"For prediction models developed on clustered data that do not account for cluster heterogeneity in model parameterization, it is crucial to use cluster-based validation to assess model generalizability on unseen clusters. This article introduces a clustered estimator of the network information criterion to approximate leave-one-cluster-out deviance for standard prediction models with twice-differentiable log-likelihood functions. The clustered network information criterion serves as a fast alternative to cluster-based cross-validation. Stone proved that the Akaike information criterion is asymptotically equivalent to leave-one-observation-out cross-validation for true parametric models with independent and identically distributed observations. Ripley noted that the network information criterion, derived from Stone's proof, is a better approximation when the model is misspecified. For clustered data, we derived clustered network information criterion by substituting the Fisher information matrix in the network information criterion with a clustering-adjusted estimator. The clustered network information criterion imposes a greater penalty when the data exhibits stronger clustering, thereby allowing the clustered network information criterion to better prevent over-parameterization. In a simulation study and an empirical example, we used standard regression to develop prediction models for clustered data with Gaussian or binomial responses. Compared to the commonly used Akaike information criterion and Bayesian information criterion for standard regression, clustered network information criterion provides a much more accurate approximation to leave-one-cluster-out deviance and results in more accurate model size and variable selection, as determined by cluster-based cross-validation, especially when the data exhibit strong clustering.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1413-1430"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144326885","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 model-free phase I/II dose optimization design for immunotherapy trials. 免疫治疗试验无模型I/II期剂量优化设计

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-01 Epub Date: 2025-05-15 DOI: 10.1177/09622802251340246

Yingjie Qiu, Mengyi Lu, Yan Han, Wenxian Zhou, Yi Zhao, Leng Han, Yong Zang

引用次数: 0

Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction. 评估基于树的集成机器学习技术用于临床风险预测的样本量要求。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-01 Epub Date: 2025-05-14 DOI: 10.1177/09622802251338983

Oya Kalaycıoğlu, Menelaos Pavlou, Serhat E Akhanlı, Mark A de Belder, Gareth Ambler, Rumana Z Omar

{"title":"Evaluating the sample size requirements of tree-based ensemble machine learning techniques for clinical risk prediction.","authors":"Oya Kalaycıoğlu, Menelaos Pavlou, Serhat E Akhanlı, Mark A de Belder, Gareth Ambler, Rumana Z Omar","doi":"10.1177/09622802251338983","DOIUrl":"10.1177/09622802251338983","url":null,"abstract":"Machine learning techniques (MLTs) are increasingly being used to develop clinical risk prediction models for binary health outcomes but the sample size requirements for developing and validating such models remain unclear. This study investigates whether sample size guidelines that target mean absolute prediction error (MAPE) for logistic regression models can be applied to tree-based ensemble MLTs (bagging, random forests, and boosting). Simulations based on two large cardiovascular datasets were used to evaluate the performance of MLTs in terms of MAPE, calibration, the C-statistic and Brier score, across six data-generating mechanisms (DGMs) and varying sample sizes. When the DGM and analysis model matched, boosting required a sample size 2-3 times larger than recommended; random forests and bagging did not achieve the target MAPE even with a 12-fold increase. For a neutral DGM that did not match any of the analysis models, logistic regression with only main effects and boosting resulted in target MAPE values with a 12-fold increase in the recommended sample size. For external validation, our simulations showed that sample size guidelines to achieve a target precision of the estimated C-statistic were suitable, and thus may be used to inform sample size calculations for MLTs.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1356-1372"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12308042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080758","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

Two-stage subsampling variable selection for sparse high-dimensional generalized linear models. 稀疏高维广义线性模型的两阶段子抽样变量选择。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-01 Epub Date: 2025-07-02 DOI: 10.1177/09622802251343597

Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gönen

{"title":"Two-stage subsampling variable selection for sparse high-dimensional generalized linear models.","authors":"Marinela Capanu, Mihai Giurcanu, Colin B Begg, Mithat Gönen","doi":"10.1177/09622802251343597","DOIUrl":"10.1177/09622802251343597","url":null,"abstract":"Although high-dimensional data analysis has received a lot of attention after the advent of omics data, model selection in this setting continues to be challenging and there is still substantial room for improvement. Through a novel combination of existing methods, we propose here a two-stage subsampling approach for variable selection in high-dimensional generalized linear regression models. In the first stage, we screen the variables using smoothly clipped absolute deviance penalty regularization followed by partial least squares regression on repeated subsamples of the data; we include in the second stage only those predictors that were most frequently selected over the subsamples either by smoothly clipped absolute deviance or for having the top loadings in either of the first two partial least squares regression components. In the second stage, we again repeatedly subsample the data and, for each subsample, we find the best Akaike information criterion model based on an exhaustive search of all possible models on the reduced set of predictors. We then include in the final model those predictors with high selection probability across the subsamples. We prove that the proposed first-stage estimator is <math><msup><mi>n</mi><mrow><mn>1</mn><mo>/</mo><mn>2</mn></mrow></msup></math>-consistent and that the true predictors are included in the first stage with probability converging to 1. In an extensive simulation study, we show that this two-stage approach outperforms the competitors yielding among the highest probability of selecting the true model while having one of the lowest number of false positives in the settings of logistic, Poisson, and linear regression. We illustrate the proposed method on two gene expression cancer datasets.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1504-1521"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144544953","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

Penalized estimation for varying coefficient additive hazards models. 变系数加性危险模型的惩罚估计。

IF 1.9 3区医学

Statistical Methods in Medical Research Pub Date : 2025-07-01 Epub Date: 2025-05-14 DOI: 10.1177/09622802251338978

Hoi Min Ng, Kin Yau Wong

{"title":"Penalized estimation for varying coefficient additive hazards models.","authors":"Hoi Min Ng, Kin Yau Wong","doi":"10.1177/09622802251338978","DOIUrl":"10.1177/09622802251338978","url":null,"abstract":"Varying coefficient models are commonly used to capture intricate interaction effects among covariates in regression models, allowing for the modification of one covariate's effect by another. Although these models offer increased flexibility, they also introduce greater estimation and computational complexity as a trade-off. This complexity is particularly evident in genomic studies, where the covariates are often high-dimensional, rendering conventional estimation methods inapplicable. In this paper, we study a penalized estimation method for the varying coefficient additive hazards model. We adopt the group lasso penalty along with the kernel smoothing technique to estimate the varying coefficients. In contrast to existing kernel methods, which only use a \"local\" neighborhood of subjects to estimate the varying coefficient function at any given point, the proposed method takes a \"global\" approach that incorporates all subjects and is more efficient. Through extensive simulation studies, we demonstrate that the proposed method produces interpretable results with satisfactory predictive performance. We provide an application to a major cancer genomic study.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1373-1384"},"PeriodicalIF":1.9,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080759","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