Journal of Biopharmaceutical Statistics最新文献_第2页

A practical analysis procedure on generalizing comparative effectiveness in the randomized clinical trial to the real-world trial-eligible population. 将随机临床试验的比较有效性推广到现实世界中符合试验条件的人群的实用分析程序。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-30 DOI: 10.1080/10543406.2025.2489282

Kuan Jiang, Xin-Xing Lai, Shu Yang, Ying Gao, Xiao-Hua Zhou

{"title":"A practical analysis procedure on generalizing comparative effectiveness in the randomized clinical trial to the real-world trial-eligible population.","authors":"Kuan Jiang, Xin-Xing Lai, Shu Yang, Ying Gao, Xiao-Hua Zhou","doi":"10.1080/10543406.2025.2489282","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489282","url":null,"abstract":"When evaluating the effectiveness of a drug, a randomized controlled trial (RCT) is often considered the gold standard due to its ability to balance effect modifiers through randomization. While RCT assures strong internal validity, its restricted external validity poses challenges in extending treatment effects to the broader real-world population due to possible heterogeneity in covariates. In this paper, we introduce a procedure to generalize the RCT findings to the real-world trial-eligible population based on the adaption of existing statistical methods. We utilized the augmented inversed probability of sampling weighting (AIPSW) estimator for the estimation and omitted variable bias framework to assess the robustness of the estimate against the assumption violation caused by potentially unmeasured confounders. We analyzed an RCT comparing the effectiveness of lowering hypertension between Songling Xuemaikang Capsule (SXC) - a traditional Chinese medicine (TCM), and Losartan as an illustration. Based on current evidence, the generalization results indicated that by adjusting covariates distribution shift, although SXC is less effective in lowering blood pressure than Losartan on week 2, there is no statistically significant difference among the trial-eligible population at weeks 4-8. In addition, sensitivity analysis further demonstrated that the generalization is robust against potential unmeasured confounders.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052946","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

Application of complete N-of-1 trial design in bioequivalence-biosimilar drug development. 完全N-of-1试验设计在生物等效性-生物类似药开发中的应用。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-29 DOI: 10.1080/10543406.2025.2489286

Yuqing Liu, Wendy Lou, Shein-Chung Chow

{"title":"Application of complete N-of-1 trial design in bioequivalence-biosimilar drug development.","authors":"Yuqing Liu, Wendy Lou, Shein-Chung Chow","doi":"10.1080/10543406.2025.2489286","DOIUrl":"10.1080/10543406.2025.2489286","url":null,"abstract":"Biosimilars play a crucial role in increasing the accessibility and affordability of biological therapies; thus, precise and reliable assessment methods are essential for their regulatory approval and clinical adoption. Currently, the 2-sequence 2-period crossover design is recommended for two-treatment biosimilar studies. However, such designs may be inadequate for the practical assessment when multiple test or reference products are involved, particularly in scenarios such as: (1) bridging biosimilar results across regulatory regions (e.g. the European Union, Canada, and United States), or (2) evaluating biosimilarity across different dosage forms or routes of administration. To address these challenges, multi-treatment designs such as Latin-square design, Williams design, and balanced incomplete block design can be considered. More recently, the complete N-of-1 trial design, which contains all permutations of treatments with replacement, has gained attention in biosimilar drug development, especially with the presence of carryover effects. However, detailed statistical methodologies and comprehensive performance comparisons of these designs are lacking in the context of multi-formulation studies. This study employs a linear mixed-effects model to estimate the contrast of treatment effects across three drug products within the framework of the designs under investigation. Subsequently, the relationship between sample size and relative efficiency is explored under same significance level and statistical power. The findings indicate that, for a given sample size, the complete N-of-1 design consistently achieves the lowest estimation variance relative to the alternative designs, thereby representing a more efficient design for biosimilar assessment under the conditions examined.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050849","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

Inverse probability weighted Bayesian dynamic borrowing for estimation of marginal treatment effects with application to hybrid control arm oncology studies. 反概率加权贝叶斯动态借用估计边际治疗效果及其在混合对照肿瘤研究中的应用。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-28 DOI: 10.1080/10543406.2025.2489285

Matthew A Psioda, Nathan W Bean, Brielle A Wright, Yuelin Lu, Alejandro Mantero, Antara Majumdar

{"title":"Inverse probability weighted Bayesian dynamic borrowing for estimation of marginal treatment effects with application to hybrid control arm oncology studies.","authors":"Matthew A Psioda, Nathan W Bean, Brielle A Wright, Yuelin Lu, Alejandro Mantero, Antara Majumdar","doi":"10.1080/10543406.2025.2489285","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489285","url":null,"abstract":"We propose an approach for constructing and evaluating the performance of inverse probability weighted robust mixture priors (IPW-RMP) which are applied to the parameters in treatment group-specific marginal models. Our framework allows practitioners to systematically study the robustness of Bayesian dynamic borrowing using the IPW-RMP to enhance the efficiency of inferences on marginal treatment effects (e.g. marginal risk difference) in a target study being planned. A key assumption motivating our work is that the data generation processes for the target study and external data source (e.g. historical study) will not be the same, likely having different distributions for key prognostic factors and possibly different outcome distributions even for individuals who have identical prognostic factors (e.g. different outcome model parameters). We demonstrate the approach using simulation studies based on both binary and time-to-event outcomes, and via a case study based on actual clinical trial data for a solid tumor cancer program. Our simulation results show that when the distribution of risk factors does in fact differ, the IPW-RMP provides improved performance compared to a standard RMP (e.g. increased power and reduced bias of the posterior mean point estimator) with essentially no loss of performance when the risk factor distributions do not differ. Thus, the IPW-RMP can safely be used in any situation where a standard RMP is appropriate.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-23"},"PeriodicalIF":1.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060133","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

rdborrow: an R package for causal inference incorporating external controls in randomized controlled trials with longitudinal outcomes. rdrborrow：一个R软件包，用于在纵向结果随机对照试验中纳入外部对照的因果推理。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-28 DOI: 10.1080/10543406.2025.2489283

Lei Shi, Herbert Pang, Chen Chen, Jiawen Zhu

{"title":"rdborrow: an R package for causal inference incorporating external controls in randomized controlled trials with longitudinal outcomes.","authors":"Lei Shi, Herbert Pang, Chen Chen, Jiawen Zhu","doi":"10.1080/10543406.2025.2489283","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489283","url":null,"abstract":"Randomized controlled trials (RCTs) are considered the gold standard for treatment effect evaluation in clinical development. However, designing and analyzing RCTs poses many challenges such as how to ensure the validity and improve the power for hypothesis testing with a limited sample size or how to account for a crossover in treatment allocation. One promising approach to circumvent these problems is to incorporate external controls from additional data sources. This manuscript introduces a new R package called rdborrow, which implements several external control borrowing methods under a causal inference framework to facilitate the design and analysis of clinical trials with longitudinal outcomes. More concretely, our package provides an Analysis module, which implements the weighting methods proposed in Zhou et al. (2024), as well as the difference-in-differences and synthetic control methods proposed in Zhou et al. (2024) for external control borrowing. Meanwhile, our package features a Simulation module which can be used to simulate trial data for study design implementation, evaluate the performance of different estimators, and conduct power analysis. In reproducible code examples, we generate simulated data sets mimicking the real data and illustrate the process users can follow to conduct simulation and analysis based on the proposed causal inference methods for randomized controlled trial data incorporating external control data.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-24"},"PeriodicalIF":1.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060147","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

Evaluating the performance of a resampling approach for internally validating the association between a time-dependent binary indicator and time-to-event outcome. 评估重新抽样方法的性能，以内部验证依赖于时间的二元指标与时间到事件结果之间的关联。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-27 DOI: 10.1080/10543406.2025.2489293

Caroline A Falvey, Jamie L Todd, Megan L Neely

{"title":"Evaluating the performance of a resampling approach for internally validating the association between a time-dependent binary indicator and time-to-event outcome.","authors":"Caroline A Falvey, Jamie L Todd, Megan L Neely","doi":"10.1080/10543406.2025.2489293","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489293","url":null,"abstract":"Identifying clinical or biological risk factors for disease plays a critical role in enabling earlier disease diagnosis, prognostic outcomes assessment, and may inform disease prevention or monitoring practices. One framework commonly examined is understanding the association between a risk factor ever occurring in follow-up and the future risk of an outcome. If such an association is found, researchers are often asked to validate the finding. External validation is often infeasible, and validation may only be performed internally. However, the performance of internal validation methods in the setting of a time-dependent binary indicator and a time-to-event outcome has not been well-studied. We emulated a dataset motivated by real-world serial biomarker observations and performed extensive simulation studies to evaluate the performance of a resampling-based method to internally validate the association between a time-dependent binary indicator and a time-to-event outcome. We found the resampling-based method achieved optimal power for validating such an association while maintaining good Type I error control.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-11"},"PeriodicalIF":1.2,"publicationDate":"2025-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040776","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

Comparison of continuous, binary, and ordinal endpoints. 连续、二进制和有序端点的比较。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-26 DOI: 10.1080/10543406.2025.2489288

Jing Zhai, Fraser Smith, Guoxing Soon

{"title":"Comparison of continuous, binary, and ordinal endpoints.","authors":"Jing Zhai, Fraser Smith, Guoxing Soon","doi":"10.1080/10543406.2025.2489288","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489288","url":null,"abstract":"Selecting the primary endpoint has been one of the most challenging tasks in the design of clinical trials. Typical endpoints include binary, continuous or time-to-event endpoints. The primary endpoint for many clinical trials is binary and is defined based on a threshold of a continuous endpoint. Many such trials could lack study power. It could be challenging to decide the appropriate threshold to define the binary endpoints; the best guess could be wrong, and the study will lose its power when that happens. For this reason, we propose to use an ordinal endpoint defined by two or more cut points as a primary or secondary efficacy endpoint when facing such challenges, to spread the risk from comparing treatment differences at a single cut point to multiple cut points. This way the study could maintain its power even if the results differ from the initial expectations. In this paper, we evaluate the performance of continuous, binary, and ordinal endpoints via extensive simulation studies. Furthermore, we compare the three types of endpoints across many clinical trials. Overall, we demonstrate that there may be some situations where the use of ordinal categorical endpoints, based on clinical and statistical considerations, could offer advantages as a primary or secondary efficacy endpoint.Disclaimer: This article has been reviewed by FDA and determined not to be consistent with the Agency's views or policies. It reflects only the views and opinions of the authors.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144006438","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

An improved biomarker-guided adaptive patient enrichment design for oncology trials. 一种改进的生物标志物引导的肿瘤试验适应性患者富集设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-23 DOI: 10.1080/10543406.2025.2489292

Zhenwei Zhou, Zhaoyang Teng, Jian Zhu, Rui Sammi Tang

{"title":"An improved biomarker-guided adaptive patient enrichment design for oncology trials.","authors":"Zhenwei Zhou, Zhaoyang Teng, Jian Zhu, Rui Sammi Tang","doi":"10.1080/10543406.2025.2489292","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489292","url":null,"abstract":"The use of biomarkers to guide adaptive enrichment designs in oncology trials presents a promising strategy for increasing trial efficiency and improving the chance of identifying efficacious treatment in the right population. With a well-defined biomarker, such designs can enhance study power and reduce costs by adapting the trial focus to promising populations. However, existing adaptive enrichment designs may not have sufficiently flexible interim decision-making rules, testing procedures, and sample size re-estimation, limiting their full potential. In this research, we propose an improved biomarker-guided adaptive enrichment design that supports dynamic interim decision-making based on treatment effects observed in biomarker-positive, biomarker-negative, and overall populations. The design includes options for early stopping for efficacy or futility in both biomarker-positive and overall populations and incorporates sample size re-estimation using an improved conditional power method to optimize study power. Simulation results show that the proposed design maintains strong control of type I error and delivers high statistical power, with a high probability of correct interim decisions in cases where treatment is effective in either the biomarker-positive or overall population. This novel framework provides a more flexible and efficient approach to conducting oncology trials with heterogenous populations, ensuring that the most appropriate patient populations are selected as the trial progresses.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2025-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144050848","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

Double machine learning methods for estimating average treatment effects: a comparative study. 估计平均治疗效果的双机器学习方法：比较研究。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-21 DOI: 10.1080/10543406.2025.2489281

Xiaoqing Tan, Shu Yang, Wenyu Ye, Douglas E Faries, Ilya Lipkovich, Zbigniew Kadziola

{"title":"Double machine learning methods for estimating average treatment effects: a comparative study.","authors":"Xiaoqing Tan, Shu Yang, Wenyu Ye, Douglas E Faries, Ilya Lipkovich, Zbigniew Kadziola","doi":"10.1080/10543406.2025.2489281","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489281","url":null,"abstract":"Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the treatment model and the outcome model via different vehicles, such as matching, weighting, and regression. The key advantage of doubly robust estimators is that they require either the treatment model or the outcome model to be correctly specified to obtain a consistent estimator of average treatment effects, and therefore lead to a more accurate and often more precise inference. However, little work has been done to understand how doubly robust estimators differ due to their unique strategies of using the treatment and outcome models and how machine learning techniques can be combined to boost their performance, which we call double machine learning estimators. Here, we examine multiple popular doubly robust methods and compare their performance using different treatment and outcome modeling via extensive simulations and a real-world application. We found that incorporating machine learning with doubly robust estimators such as the targeted maximum likelihood estimator gives the best overall performance. Practical guidance on how to apply doubly robust estimators is provided.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043845","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

Joint modeling of longitudinal endpoints and its applications to trial planning, monitoring and analysis. 纵向端点联合建模及其在试验计划、监测和分析中的应用。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-20 DOI: 10.1080/10543406.2025.2489280

Liangcai Zhang, George Capuano, Vladimir Dragalin, John Jezorwski, Kim Hung Lo, Fei Chen

{"title":"Joint modeling of longitudinal endpoints and its applications to trial planning, monitoring and analysis.","authors":"Liangcai Zhang, George Capuano, Vladimir Dragalin, John Jezorwski, Kim Hung Lo, Fei Chen","doi":"10.1080/10543406.2025.2489280","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489280","url":null,"abstract":"In the context of clinical trial practices, the study power and sample size are typically determined based on the expected treatment effects on the primary endpoint collected over time. The utilization of longitudinal modeling for the primary endpoint offers a flexible approach that has the potential to reduce the sample size and duration of the trial, thereby improving operational efficiency and costs. Joint modeling of multiple endpoints presents a unique opportunity to understand how the primary endpoint evolves over time with other clinically important endpoints, and has the potential to increase precision of estimates and therefore increase study power when designing a study at planning stage and enhance understanding and interpretation of the data at a multi-dimensional level at the analysis stage. This approach enables a comprehensive evaluation of clinical evidence from various perspectives, rather than relying solely on isolated pieces of information. Joint modeling of multiple longitudinal endpoints would also help trial monitoring process as the trial accumulates clinical evidence of efficacy data, and there is a high demand in developing tools for statistical learning the treatment benefits on the go especially when the endpoint(s) is not well-established yet in some therapeutic indications. In this article, we will illustrate the use of joint modeling of longitudinal endpoints and its applications to study design, analysis, and trial monitoring practices. Simulation studies suggest that the potential efficiency gain would be achieved via leveraging information within endpoint over time and/or between endpoints. We developed an R shiny application to aid in and support identifying promising efficacy signals from endpoints under investigation during the trial monitoring. The implementation of the joint models and the added values will be discussed through case studies and/or simulation studies.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041925","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

Biomarker-guided adaptive enrichment design with threshold detection for clinical trials with time-to-event outcome. 生物标志物引导的自适应富集设计与阈值检测的临床试验与时间到事件的结果。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-04-20 DOI: 10.1080/10543406.2025.2489291

Kaiyuan Hua, Hwanhee Hong, Xiaofei Wang

{"title":"Biomarker-guided adaptive enrichment design with threshold detection for clinical trials with time-to-event outcome.","authors":"Kaiyuan Hua, Hwanhee Hong, Xiaofei Wang","doi":"10.1080/10543406.2025.2489291","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489291","url":null,"abstract":"Biomarker-guided designs are increasingly used to evaluate personalized treatments based on patients' biomarker status in Phase II and III clinical trials. With adaptive enrichment, these designs can improve the efficiency of evaluating the treatment effect in biomarker-positive patients by increasing their proportion in the randomized trial. While time-to-event outcomes are often used as the primary endpoint to measure treatment effects for a new therapy in severe diseases like cancer and cardiovascular diseases, there is limited research on biomarker-guided adaptive enrichment trials in this context. Such trials almost always adopt hazard ratio methods for statistical measurement of treatment effects. In contrast, restricted mean survival time (RMST) has gained popularity for analyzing time-to-event outcomes because it offers more straightforward interpretations of treatment effects and does not require the proportional hazard assumption. This paper proposes a two-stage biomarker-guided adaptive RMST design with threshold detection and patient enrichment. We develop sophisticated methods for identifying the optimal biomarker threshold and biomarker-positive subgroup, treatment effect estimators, and approaches for type I error rate, power analysis, and sample size calculation. We present a numerical example of re-designing an oncology trial. An extensive simulation study is conducted to evaluate the performance of the proposed design.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058373","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