Statistics in Medicine最新文献_第5页

Tutorial on Bayesian Functional Regression Using Stan. 使用Stan的贝叶斯函数回归教程。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70265

Ziren Jiang, Ciprian Crainiceanu, Erjia Cui

引用次数: 0

Using Confidence Distributions in Final and Interim Analyses for Single-Arm Studies or Platform Trials Consisting of Single-Arm Studies. 在单组研究或由单组研究组成的平台试验的最终和中期分析中使用置信分布。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70251

Günter Heimann, Peter Jacko, Tom Parke

{"title":"Using Confidence Distributions in Final and Interim Analyses for Single-Arm Studies or Platform Trials Consisting of Single-Arm Studies.","authors":"Günter Heimann, Peter Jacko, Tom Parke","doi":"10.1002/sim.70251","DOIUrl":"https://doi.org/10.1002/sim.70251","url":null,"abstract":"Confidence distributions are a frequentist alternative to the Bayesian posterior distribution. These confidence distributions have received more attention in the recent past because of their simplicity. In rare diseases, oncology, or in pediatric drug development, single-arm trials, or platform trials consisting of a series of single-arm trials are increasingly being used, both to establish proof-of-concept and to provide pivotal evidence for a marketing application. Often, these single-arm trials are designed as two-stage designs, or they include sequential or continuous monitoring approaches. They are analyzed using standard frequentist, Bayesian, or other methods. In this paper, we describe how to define analysis strategies based on confidence distributions for such single-arm trials or for platform trials that consist of a series of single arm trials. We focus on binary endpoints and show how to define the corresponding decision rules for final and interim analyses and how to derive their operating characteristics exactly, for example, without simulation. Our approach uses predictive probabilities rather than conditional probabilities (as with stochastic curtailment) to define the interim decision rules. It can be applied to platform, basket, and umbrella trials that consist of a series of single-arm trials but also to stand-alone single arm trials.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70251"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145030503","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Weighted Trigonometric Regression for Suboptimal Designs in Circadian Transcriptome Studies. 昼夜转录组研究中次优设计的加权三角回归。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70201

Michael T Gorczyca, Justice D Sefas

{"title":"Weighted Trigonometric Regression for Suboptimal Designs in Circadian Transcriptome Studies.","authors":"Michael T Gorczyca, Justice D Sefas","doi":"10.1002/sim.70201","DOIUrl":"10.1002/sim.70201","url":null,"abstract":"Circadian transcriptome studies often use trigonometric regression to model gene expression over time. Ideally, protocols in these studies would collect tissue samples at evenly distributed and equally spaced time points over a 24-hour period. This sample collection protocol is known as an equispaced design, which is considered the optimal experimental design for trigonometric regression under multiple statistical criteria. However, implementing equispaced designs in studies involving individuals is logistically challenging, and failure to employ an equispaced design could introduce variability in the statistical power of a hypothesis test relative to a model's phase-shift parameter estimates. This article is motivated by the variability in power for hypothesis testing when tissue samples are not collected under an equispaced design, and considers a weighted trigonometric regression as a remedy. Specifically, the weights for this regression are the normalized reciprocals of estimates derived from a kernel density estimator for sample collection time, which inflates the weight of samples collected at underrepresented time points. A search procedure is also introduced to identify the hyperparameter for kernel density estimation that relates to maximizing the smallest eigenvalue of the Hessian of weighted squared loss, which is motivated by the <math> <semantics><mrow><mi>E</mi></mrow> <annotation>$$ E $$</annotation></semantics> </math> -optimality criterion from experimental design literature. Simulation studies consistently demonstrate that this weighted regression mitigates variability in power for hypothesis tests performed with an estimated model. Illustrations with six circadian transcriptome datasets further indicate that this weighted regression consistently yields larger test statistics than its unweighted counterpart for first-order trigonometric regression, or cosinor regression, which is prevalent in circadian transcriptome studies.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70201"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145065523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Biostatisticians Meet AI: Navigating Shifts While Preserving Principles. 生物统计学家与人工智能相遇：在保持原则的同时导航变化。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70271

Bin Zhu

引用次数: 0

Double Negative Control Inference With Some Invalid Negative Control Exposures for Continuous Outcome. 连续结果的双负控制推断与一些无效负控制暴露。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70276

Qingqing Yang, Jinzhu Jia

{"title":"Double Negative Control Inference With Some Invalid Negative Control Exposures for Continuous Outcome.","authors":"Qingqing Yang, Jinzhu Jia","doi":"10.1002/sim.70276","DOIUrl":"https://doi.org/10.1002/sim.70276","url":null,"abstract":"Negative controls have been increasingly used for causal inference when unmeasured confounding exist. Valid negative control exposures (NCEs) could not causally affect outcome, and valid negative control outcomes (NCOs) are not to be causally affected by exposure. In most observational studies, it is easy to find a valid NCO but NCEs are harder to verify due to the current limited knowledge. Invalid NCEs associated with outcome result in biased estimate of causal effects. However, previous work considering invalid negative controls is very limited. In this paper, we develop a double negative control framework for continuous outcomes in the presence of some invalid NCEs. First, we prove that it is possible to identify causal effects with a known pre-defined valid NCO and a pre-defined set of NCEs without knowing exactly their validity. Furthermore, as long as more than 50% of NCEs are valid, the average causal effect could be consistently estimated. Then we design an <math> <semantics> <mrow><msub><mi>ℓ</mi> <mn>1</mn></msub> </mrow> <annotation>$$ {mathrm{ell}}_1 $$</annotation></semantics> </math> procedure to select valid NCEs. Finally, we give two kinds of double negative control estimators (sisvNCE and naiveNCE-Median) with a guarantee of theoretical estimation performance. Simulation results show that the performance of our method is robust when the number of invalid NCEs does not exceed a certain threshold. Application results indicate that our method has a promising role in public health.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70276"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145125925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Futility Analyses for the MCP-Mod Methodology Based on Longitudinal Models. 基于纵向模型的MCP-Mod方法的有效性分析。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70274

Björn Bornkamp, Jie Zhou, Dong Xi, Weihua Cao

引用次数: 0

Towards Efficient Time-to-Event Dose-Escalation Guidance of Multi-Cycle Cancer Therapies. 多周期癌症治疗的有效时间-事件剂量递增指导。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70229

Lukas A Widmer, Sebastian Weber, Yunnan Xu, Hans-Jochen Weber

{"title":"Towards Efficient Time-to-Event Dose-Escalation Guidance of Multi-Cycle Cancer Therapies.","authors":"Lukas A Widmer, Sebastian Weber, Yunnan Xu, Hans-Jochen Weber","doi":"10.1002/sim.70229","DOIUrl":"10.1002/sim.70229","url":null,"abstract":"Treatment of cancer has rapidly evolved over time in quite dramatic ways, for example, from chemotherapies, targeted therapies to immunotherapies and chimeric antigen receptor T-cells. Nonetheless, the basic design of early phase I trials in oncology still follows predominantly a dose-escalation design. These trials monitor safety over the first treatment cycle to escalate the dose of the investigated drug. However, over time, studying additional factors such as drug combinations and/or variation in the timing of dosing became important as well. Existing designs were continuously enhanced and expanded to account for increased trial complexity. With toxicities occurring at later stages beyond the first cycle and the need to treat patients over multiple cycles, the focus on the first treatment cycle only is becoming a limitation in nowadays multi-cycle treatment therapies. Here, we introduce a multi-cycle time-to-event model (TITE-CLRM: Time-Interval-To-Event Complementary-Loglog Regression Model), allowing guidance of dose-escalation trials studying multi-cycle therapies. The challenge lies in balancing the need to monitor the safety of longer treatment periods with the need to continuously enroll patients safely. The proposed multi-cycle time-to-event model is formulated as an extension to established concepts like the escalation with overdose control principle. The model is motivated by a current drug development project and evaluated in a simulation study.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70229"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145070318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Bayesian Adaptive Enrichment Design for Continuous Biomarkers. 连续生物标志物的贝叶斯自适应富集设计。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70262

Yue Tu, Yusha Liu, Wendy J Mack, Lindsay A Renfro

引用次数: 0

The Estimand Framework in Diagnostic Accuracy Studies. 诊断准确性研究中的估计框架。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70248

Alexander Fierenz, Mouna Akacha, Norbert Benda, Mahnaz Badpa, Patrick M M Bossuyt, Nandini Dendukuri, Britta Rackow, Antonia Zapf

{"title":"The Estimand Framework in Diagnostic Accuracy Studies.","authors":"Alexander Fierenz, Mouna Akacha, Norbert Benda, Mahnaz Badpa, Patrick M M Bossuyt, Nandini Dendukuri, Britta Rackow, Antonia Zapf","doi":"10.1002/sim.70248","DOIUrl":"10.1002/sim.70248","url":null,"abstract":"Diagnostic accuracy studies evaluate how well a diagnostic test can detect or rule out a medical condition. Different events can interfere with the conduct of the test, affecting the test result. Before starting a diagnostic test accuracy study, the clinical question of interest should be precisely defined. Based on that, strategies can be chosen for dealing with the interfering event. We introduce six different strategies for how such events could be handled. We introduce the estimand concept for diagnostic accuracy studies, which consists of the attributes population, target condition, index test, accuracy measure, and the strategies for handling interfering events. The estimand determines which effect is estimated based on the study objective. To bridge the gap between the clinical study objective and the method for the estimation, we illustrate the necessary steps using a fictitious computed tomography scan study. The defined estimand improves the structure of the planning phase, enhances the interdisciplinary exchange, and supports the interpretation based on the study objective.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70248"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12442747/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145076026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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

Model-Robust Standardization in Cluster-Randomized Trials. 聚类随机试验的模型稳健标准化。

IF 1.8 4区医学

Statistics in Medicine Pub Date : 2025-09-01 DOI: 10.1002/sim.70270

Fan Li, Jiaqi Tong, Xi Fang, Chao Cheng, Brennan C Kahan, Bingkai Wang

{"title":"Model-Robust Standardization in Cluster-Randomized Trials.","authors":"Fan Li, Jiaqi Tong, Xi Fang, Chao Cheng, Brennan C Kahan, Bingkai Wang","doi":"10.1002/sim.70270","DOIUrl":"https://doi.org/10.1002/sim.70270","url":null,"abstract":"In cluster-randomized trials, generalized linear mixed models and generalized estimating equations have conventionally been the default analytic methods for estimating the average treatment effect as routine practice. However, recent studies have demonstrated that their treatment effect coefficient estimators may correspond to ambiguous estimands when the models are misspecified or when there exist informative cluster sizes. In this article, we present a unified approach that standardizes output from a given regression model to ensure estimand-aligned inference for the treatment effect parameters in cluster-randomized trials. We introduce estimators for both the cluster-average and the individual-average treatment effects (marginal estimands) that are always consistent regardless of whether the specified working regression models align with the unknown data generating process. We further explore the use of a deletion-based jackknife variance estimator for inference. The development of our approach also motivates a natural test for informative cluster size. Extensive simulation experiments are designed to demonstrate the advantage of the proposed estimators under a variety of scenarios. The proposed model-robust standardization methods are implemented in the MRStdCRT R package.","PeriodicalId":21879,"journal":{"name":"Statistics in Medicine","volume":"44 20-22","pages":"e70270"},"PeriodicalIF":1.8,"publicationDate":"2025-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145087326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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