Statistical Methods in Medical Research最新文献_第3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 : 2025-05-14 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":"https://doi.org/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":"9622802251334886"},"PeriodicalIF":1.6,"publicationDate":"2025-05-14","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 1.6 3区医学

Statistical Methods in Medical Research Pub 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":"https://doi.org/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":"9622802251338387"},"PeriodicalIF":1.6,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","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":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Analysis of recurrent events in cluster randomised trials: The PLEASANT trial case study. 聚类随机试验中复发事件的分析：PLEASANT试验案例研究。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-14 DOI: 10.1177/09622802251316972

Kelly Grant, Steven A Julious

{"title":"Analysis of recurrent events in cluster randomised trials: The PLEASANT trial case study.","authors":"Kelly Grant, Steven A Julious","doi":"10.1177/09622802251316972","DOIUrl":"10.1177/09622802251316972","url":null,"abstract":"Recurrent events for many clinical conditions, such as asthma, can indicate poor health outcomes. Recurrent events data are often analysed using statistical methods such as Cox regression or negative binomial regression, suffering event or time information loss. This article re-analyses the preventing and lessening exacerbations of asthma in school-age children associated with a new term (PLEASANT) trial data as a case study, investigating the utility, extending recurrent events survival analysis methods to cluster randomised trials. A conditional frailty model is used, with the frailty term at the general practitioner practice level, accounting for clustering. A rare events bias adjustment is applied if few participants had recurrent events and truncation of small event risk sets is explored, to improve model accuracy. Global and event-specific estimates are presented, alongside a mean cumulative function plot to aid interpretation. The conditional frailty model global results are similar to PLEASANT results, but with greater precision (include time, recurrent events, within-participant dependence, and rare events adjustment). Event-specific results suggest an increasing risk reduction in medical appointments for the intervention group, in September-December 2013, as medical contacts increase over time. The conditional frailty model is recommended when recurrent events are a study outcome for clinical trials, including cluster randomised trials, to help explain changes in event risk over time, assisting clinical interpretation.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251316972"},"PeriodicalIF":1.6,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080727","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 1.6 3区医学

Statistical Methods in Medical Research Pub 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":"https://doi.org/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":"9622802251338978"},"PeriodicalIF":1.6,"publicationDate":"2025-05-14","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

A family of Bayesian prognostic and predictive covariate-adjusted response-adaptive randomization designs. 一系列贝叶斯预测和预测协变量调整反应-自适应随机化设计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-14 DOI: 10.1177/09622802251335150

Xinyi Pei, Yujie Zhao, Jun Yu, Li Wang, Hongjian Zhu

{"title":"A family of Bayesian prognostic and predictive covariate-adjusted response-adaptive randomization designs.","authors":"Xinyi Pei, Yujie Zhao, Jun Yu, Li Wang, Hongjian Zhu","doi":"10.1177/09622802251335150","DOIUrl":"https://doi.org/10.1177/09622802251335150","url":null,"abstract":"The prudent use of covariates to enhance the efficiency and ethics of clinical trials has garnered significant attention, particularly following the FDA's 2023 guidance on adjusting for covariates. This article introduces a Bayesian covariate-adjusted response-adaptive design aimed at distinguishing between prognostic and predictive covariates during randomization and analysis. The proposed design allocates more patients to the superior treatment based on predictive covariates while maintaining balance across prognostic covariate levels, without sacrificing the power to detect treatment effects. Predictive covariates, which identify patients more likely to benefit from a treatment, and prognostic covariates, which predict overall clinical outcomes, are crucial for personalized medicine and ethical rigor in clinical trials. The Bayesian covariate-adjusted response-adaptive design leverages these covariates to enhance precision and ensure balanced comparison groups, addressing patient heterogeneity and improving treatment efficacy. Our approach builds on the foundation of response-adaptive randomization designs, incorporating Bayesian methodologies to manage the complexities of adaptive designs and control the Type I error rate. Comprehensive numerical studies demonstrate the advantages of our design in achieving ethical, efficient, and balancing goals.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"9622802251335150"},"PeriodicalIF":1.6,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080612","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 global average treatment effect in National Heart, Lung, and Blood Institute (NHLBI) Growth and Health Study. 国家心脏、肺和血液研究所（NHLBI）生长和健康研究中全球平均治疗效果的估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-04-13 DOI: 10.1177/09622802241313288

Lili Yue, Colin O Wu, Gaorong Li, Zhaohai Li

{"title":"Estimation of global average treatment effect in National Heart, Lung, and Blood Institute (NHLBI) Growth and Health Study.","authors":"Lili Yue, Colin O Wu, Gaorong Li, Zhaohai Li","doi":"10.1177/09622802241313288","DOIUrl":"10.1177/09622802241313288","url":null,"abstract":"We propose a procedure to estimate the \"time-specific average treatment effect\" and \"global average treatment effect\" for observational studies with outcomes and covariates repeatedly measured over time. This research is motivated by the National Heart, Lung and Blood Institute Growth and Health Study (NGHS), a longitudinal cohort study that aims to evaluate the influences of race and other risk factors on the levels of blood pressure for children and adolescents. As with most longitudinal cohort studies, we do not have a known propensity score model to further discuss the average treatment effects in the NGHS. To solve this problem, a nonparametric machine learning method, the generalized boosted models (GBMs), is used to estimate the propensity score. Based on the estimated propensity score, the \"time-specific average treatment effect\" can be obtained through the inverse probability weighting methods, then the \"global average treatment effect\" is also obtained. We apply the proposed GBM-based estimation method to the NGHS blood pressure data and demonstrate through a simulation study that the GBM-based estimation method is superior to the commonly used logistic regression-based method.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"956-967"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144034767","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

Effect estimation in the presence of a misclassified binary mediator. 存在错误分类的二元介质时的效应估计。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-03-20 DOI: 10.1177/09622802251316970

Kimberly A Hochstedler Webb, Martin T Wells

{"title":"Effect estimation in the presence of a misclassified binary mediator.","authors":"Kimberly A Hochstedler Webb, Martin T Wells","doi":"10.1177/09622802251316970","DOIUrl":"10.1177/09622802251316970","url":null,"abstract":"Mediation analyses allow researchers to quantify the effect of an exposure variable on an outcome variable through a mediator variable. If a binary mediator variable is misclassified, the resulting analysis can be severely biased. Misclassification is especially difficult to deal with when it is differential and when there are no gold standard labels available. Previous work has addressed this problem using a sensitivity analysis framework or by assuming that misclassification rates are known. We leverage a variable related to the misclassification mechanism to recover unbiased parameter estimates without using gold standard labels. The proposed methods require the reasonable assumption that the sum of the sensitivity and specificity is greater than 1. Three correction methods are presented: (1) An ordinary least squares correction for Normal outcome models, (2) a multi-step predictive value weighting method, and (3) a seamless expectation-maximization algorithm. We apply our misclassification correction strategies to investigate the mediating role of gestational hypertension on the association between maternal age and pre-term birth.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"1037-1059"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670956","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

Hierarchical Bayesian bivariate spatial modeling of small area proportions with application to health survey data. 应用于健康调查数据的小面积比例的层次贝叶斯双变量空间建模。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-02-11 DOI: 10.1177/09622802251316968

Hanjun Yu, Xinyi Xu, Lichao Yu

引用次数: 0

Statistical considerations for evaluating treatment effect under various non-proportional hazard scenarios. 在各种非比例危险情景下评估治疗效果的统计考虑。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-02-11 DOI: 10.1177/09622802241313297

Xinyu Zhang, Erich J Greene, Ondrej Blaha, Wei Wei

{"title":"Statistical considerations for evaluating treatment effect under various non-proportional hazard scenarios.","authors":"Xinyu Zhang, Erich J Greene, Ondrej Blaha, Wei Wei","doi":"10.1177/09622802241313297","DOIUrl":"10.1177/09622802241313297","url":null,"abstract":"We conducted a systematic comparison of statistical methods used for the analysis of time-to-event outcomes under various proportional and non-proportional hazard (NPH) scenarios. Our study used data from recently published oncology trials to compare the Log-rank test, still by far the most widely used option, against some available alternatives, including the MaxCombo test, the Restricted Mean Survival Time difference test, the Generalized Gamma model and the Generalized F model. Power, type I error rate, and time-dependent bias with respect to the survival probability and median survival time were used to evaluate and compare the performance of these methods. In addition to the real data, we simulated three hypothetical scenarios with crossing hazards chosen so that the early and late effects \"cancel out\" and used them to evaluate the ability of the aforementioned methods to detect time-specific and overall treatment effects. We implemented novel metrics for assessing the time-dependent bias in treatment effect estimates to provide a more comprehensive evaluation in NPH scenarios. Recommendations under each NPH scenario are provided by examining the type I error rate, power, and time-dependent bias associated with each statistical approach.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"986-1000"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143392085","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

Using shrinkage methods to estimate treatment effects in overlapping subgroups in randomized clinical trials with a time-to-event endpoint. 在具有事件时间终点的随机临床试验中，使用收缩法估计重叠亚组的治疗效果。

IF 1.6 3区医学

Statistical Methods in Medical Research Pub Date : 2025-05-01 Epub Date: 2025-03-25 DOI: 10.1177/09622802241313292

Marcel Wolbers, Mar Vázquez Rabuñal, Ke Li, Kaspar Rufibach, Daniel Sabanés Bové

{"title":"Using shrinkage methods to estimate treatment effects in overlapping subgroups in randomized clinical trials with a time-to-event endpoint.","authors":"Marcel Wolbers, Mar Vázquez Rabuñal, Ke Li, Kaspar Rufibach, Daniel Sabanés Bové","doi":"10.1177/09622802241313292","DOIUrl":"10.1177/09622802241313292","url":null,"abstract":"In randomized controlled trials, forest plots are frequently used to investigate the homogeneity of treatment effect estimates in pre-defined subgroups. However, the interpretation of subgroup-specific treatment effect estimates requires great care due to the smaller sample size of subgroups and the large number of investigated subgroups. Bayesian shrinkage methods have been proposed to address these issues, but they often focus on disjoint subgroups while subgroups displayed in forest plots are overlapping, i.e., each subject appears in multiple subgroups. In our proposed approach, we first build a flexible Cox model based on all available observations, including treatment-by-subgroup interaction terms for all subgroups of interest. We explore penalized partial likelihood estimation with lasso or ridge penalties for interaction terms, and Bayesian estimation with a regularized horseshoe prior. In a second step, the Cox model is marginalized to obtain treatment effect estimates for all subgroups. We illustrate these methods using data from a randomized clinical trial in follicular lymphoma and evaluate their properties in a simulation study. In all simulation scenarios, the overall mean-squared error is substantially smaller for penalized and shrinkage estimators compared to the standard subgroup-specific treatment effect estimator but leads to some bias for heterogeneous subgroups. A naive overall sample estimator also outperforms the standard subgroup-specific estimator in terms of the overall mean-squared error for all scenarios except for a scenario with substantial heterogeneity. We recommend that subgroup-specific estimators are routinely complemented by treatment effect estimators based on shrinkage methods. The proposed methods are implemented in the R package bonsaiforest.","PeriodicalId":22038,"journal":{"name":"Statistical Methods in Medical Research","volume":" ","pages":"903-914"},"PeriodicalIF":1.6,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143701487","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