Journal of Biopharmaceutical Statistics最新文献

Estimand-based design strategies for cardiovascular recurrent event trials of combination therapies. 基于评估的心血管复发事件联合治疗试验设计策略。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-28 DOI: 10.1080/10543406.2026.2747684

Ayon Mukherjee

{"title":"Estimand-based design strategies for cardiovascular recurrent event trials of combination therapies.","authors":"Ayon Mukherjee","doi":"10.1080/10543406.2026.2747684","DOIUrl":"https://doi.org/10.1080/10543406.2026.2747684","url":null,"abstract":"Cardiovascular trials increasingly evaluate combination therapies in settings where patients may experience recurrent events such as myocardial infarction, heart failure hospitalization, or stroke. Traditional single-event analyses fail to capture the cumulative disease burden and temporal dynamics, motivating the integration of recurrent event methods within an estimand framework. This manuscript reviews estimand strategies for recurrent event analyses in cardiovascular trials of combination therapies, emphasizing alignment between clinical questions, intercurrent events, and causal interpretation. We compare commonly used models, including the Andersen-Gill, Prentice-Williams-Peterson, and Negative Binomial approaches, highlighting their suitability for different estimands. We distinguish each model's conditional or marginal estimand, its identifiability assumptions, and the difference between sensitivity analyses that vary assumptions under the same estimand and supplementary analyses that target alternative estimands with different intercurrent event strategies. The impact of intercurrent events such as treatment discontinuation, rescue therapy, and mortality on estimand interpretation is explored. Novel methodological considerations specific to combination-therapy trials, including component-specific intercurrent events (ICE), drug-drug interaction estimands, mixed intercurrent event (ICE) strategies, and principal-stratum estimands conditional on dual adherence, are formally defined and discussed with work examples. We further discuss how the choice of estimand influences causal inference and model selection. Finally, we provide a regulatory perspective on estimand-based approaches in cardiovascular trials. This manuscript serves as a methodological guide for designing and analyzing cardiovascular trials with recurrent events for combination therapies to ensure clarity of clinical interpretation, robustness of causal conclusions, and regulatory alignment.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-40"},"PeriodicalIF":1.3,"publicationDate":"2026-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149290051","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

A robust meta-analytic approach for evaluating regional treatment effect in a multi-regional clinical trial. 在多区域临床试验中评估区域治疗效果的可靠meta分析方法。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-26 DOI: 10.1080/10543406.2026.2741416

Naoki Isogawa, Satrajit Roychoudhury, Marc Vandemeulebroecke, Foteini Strimenopoulou, Wenjin Wang

{"title":"A robust meta-analytic approach for evaluating regional treatment effect in a multi-regional clinical trial.","authors":"Naoki Isogawa, Satrajit Roychoudhury, Marc Vandemeulebroecke, Foteini Strimenopoulou, Wenjin Wang","doi":"10.1080/10543406.2026.2741416","DOIUrl":"https://doi.org/10.1080/10543406.2026.2741416","url":null,"abstract":"A multi-regional clinical trial under one protocol is the gold standard in global drug development. In addition to the positive benefit-risk ratio in the overall population, evaluating local treatment effects and assessing regional consistency are significant components of labeling discussions with the local health authorities. However, relatively small sample sizes in some regions lead to lower precision in the local treatment effect estimates. Such hurdles can be overcome by using results from other regions while estimating the region-specific treatment effect. Commonly used methods are complete pooling of data from specific regions assuming constant effect, and a Bayesian hierarchical model assuming exchangeability or similarity. Pooling patient data from multiple regions increases the precision of the treatment effect estimated in one region provided the medical practice is similar across regions. However, such an approach ignores heterogeneity between different regions and can cause an unrealistic estimate. On the other hand, the Bayesian hierarchical model considers the between-region heterogeneity and provides shrinkage estimates for region-specific treatment effects. However, shrinkage estimators are often criticized for providing too optimistic information borrowing in the presence of outliers. This article presents a robust meta-analytic approach for estimating region-specific treatment effects. While improving the precision, the proposed assessment is less influenced by other regions and provides a more robust estimation of the treatment effect. Simulation studies and a real-life example illustrate the proposed methodology.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-21"},"PeriodicalIF":1.3,"publicationDate":"2026-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149277330","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

Beyond treatment selection: Interim hypothesis addition and deletion in confirmatory seamless designs. 超越治疗选择：验证性无缝设计的临时假设添加和删除。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-24 DOI: 10.1080/10543406.2026.2747695

Krishna Padmanabhan, Cyrus Mehta, Pranav Yajnik

{"title":"Beyond treatment selection: Interim hypothesis addition and deletion in confirmatory seamless designs.","authors":"Krishna Padmanabhan, Cyrus Mehta, Pranav Yajnik","doi":"10.1080/10543406.2026.2747695","DOIUrl":"https://doi.org/10.1080/10543406.2026.2747695","url":null,"abstract":"Inferentially seamless Phase 2/3 designs can accelerate drug development by combining learning and confirmatory objectives within a single protocol while preserving strict Type I control. Standard multiplicity frameworks for such trials typically assume that the complete set of efficacy hypotheses is fixed at trial initiation. In practice, however, emerging internal or external evidence can motivate the introduction of new confirmatory hypotheses at an interim analysis (IA), for example by adding a new dose that was not initially planned for confirmatory testing. This creates a tension between operational flexibility and rigorous Type I error control, a statistical and regulatory requirement. We extend the conditional error rate (CER) framework to permit hypothesis addition and deletion at an IA while maintaining strong familywise error rate (FWER) control under a closed testing principle. We also derive the conditional covariance structures and reference distributions needed when intersection hypotheses involve a newly introduced component. We compare our CER procedure to (i) an inverse-normal stagewise <math><mi>p</mi></math>-value combination approach and (ii) a straightforward split-<math><mi>α</mi></math> comparator that allocates separate one-sided error to \"old\" versus newly introduced hypotheses. We evaluate operating characteristics in a two-stage seamless setting with interim best-dose selection and an added dose at the IA. Simulations under the global null and under partial-null configurations support the strong FWER control of the implementation, while sensitivity analyses examine misspecification of the working reference distribution. Across alternative scenarios, the CER procedure can provide meaningful power gains, especially when the newly introduced dose is promising.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-19"},"PeriodicalIF":1.3,"publicationDate":"2026-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149277144","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

ANPP: The adapted normalized power prior for borrowing information from multiple historical datasets in clinical trials. ANPP：在临床试验中从多个历史数据集中借用信息的适应性归一化功率先验。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-22 DOI: 10.1080/10543406.2026.2744717

Yueqi Shen, Matthew A Psioda, Luiz M Carvalho, Joseph G Ibrahim

{"title":"ANPP: The adapted normalized power prior for borrowing information from multiple historical datasets in clinical trials.","authors":"Yueqi Shen, Matthew A Psioda, Luiz M Carvalho, Joseph G Ibrahim","doi":"10.1080/10543406.2026.2744717","DOIUrl":"https://doi.org/10.1080/10543406.2026.2744717","url":null,"abstract":"The power prior is a popular class of informative priors for incorporating information from historical data. It involves raising the likelihood for the historical data to a power, which acts as a discounting parameter. When the discounting parameter is modeled as random, the normalized power prior (NPP) is recommended. When there are multiple historical datasets, there has been limited research on how to choose priors for the multiple discounting parameters of the NPP to induce desirable information borrowing behavior. In this work, we address this question by investigating the analytical relationship between the NPP and the Bayesian hierarchical model (BHM), which is a widely used method for synthesizing information from different sources. We develop the adapted normalized power prior (ANPP), which establishes dependence between the dataset-specific discounting parameters of the NPP, leading to inferences that are identical to the BHM. We establish a direct relationship between the prior for the dataset-specific discounting parameters of the ANPP and the prior for the variance parameter of the BHM. Establishing this relationship not only justifies the NPP from the perspective of hierarchical modeling but also achieves easy prior elicitation for the NPP for the purpose of dynamic borrowing. We examine the borrowing properties of the ANPP through simulations and apply it to a case study for a pediatric lupus trial.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.3,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149247435","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

Estimating the joint distribution of two binary variables from marginal summaries in clinical trials. 从临床试验的边缘总结中估计两个二元变量的联合分布。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-22 DOI: 10.1080/10543406.2026.2745677

Longwen Shang, Min Tsao, Zengliang Gao, Xuekui Zhang

{"title":"Estimating the joint distribution of two binary variables from marginal summaries in clinical trials.","authors":"Longwen Shang, Min Tsao, Zengliang Gao, Xuekui Zhang","doi":"10.1080/10543406.2026.2745677","DOIUrl":"https://doi.org/10.1080/10543406.2026.2745677","url":null,"abstract":"Clinical trial simulation (CTS) is critical in new drug development, providing insight into safety and efficacy while guiding trial design before committing substantial investment. Achieving realistic outcomes in CTS requires an accurately estimated joint distribution of the underlying variables. However, privacy concerns and data availability issues often restrict researchers to marginal summary-level data of each variable obtained from publications, making it challenging to estimate the joint distribution due to the lack of access to individual-level data or relational summaries between variables. We propose a novel maximum likelihood approach to estimate the joint distribution of two binary variables using only marginal summary data from multiple published studies. By leveraging numerical optimization and accommodating varying sample sizes across studies, our method preserves privacy while bypassing the need for granular or relational data. Through an extensive simulation study covering a diverse range of scenarios and an application to a real-world dataset, we demonstrate the accuracy, robustness, and practicality of our method. This method enhances the simulation of realistic clinical trial data, thereby improving decision-making processes in drug development. To facilitate its implementation, we provide an open-source R package, ebdm, which is available on CRAN.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.3,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149277170","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

Simultaneously leveraging individual- and summary-level external data in clinical trials. 同时在临床试验中利用个人和汇总级的外部数据。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-22 DOI: 10.1080/10543406.2026.2741425

Jinmei Chen, Yongfu Yu, Guoyou Qin

{"title":"Simultaneously leveraging individual- and summary-level external data in clinical trials.","authors":"Jinmei Chen, Yongfu Yu, Guoyou Qin","doi":"10.1080/10543406.2026.2741425","DOIUrl":"https://doi.org/10.1080/10543406.2026.2741425","url":null,"abstract":"With the growing availability of external data (e.g., real-world data (RWD) and historical data), there is increasing interest in integrating external data into Randomized Controlled Trials (RCT), especially for rare diseases with high unmet medical needs where traditional clinical trials would be impractical and/or unethical. However, despite the increasing availability of external data, the level of detail varies across sources, as some provide individual-level patient records while others offer only aggregated summary statistics, often due to privacy and regulatory constraints. Most existing statistical methods focus on leveraging either individual-level or summary-level external data exclusively, with relatively few approaches addressing the simultaneous integration of both data types. Therefore, we propose a Bayesian method that integrates existing approaches for borrowing individual-level and summary-level external data to augment the control arm of RCT, enabling their joint use through Bayesian updating. Specifically, we combine inverse probability weighting (IPW) with Bayesian power prior for individual-level data, and the unit information prior (UIP) for summary-level data. Simulation results demonstrate that the proposed method maintains type I error control at the nominal level and improves statistical power, while remaining straightforward to implement. Its flexible structure also allows the components of the proposed method to be replaced with alternative existing methods that leverage either individual-level or summary-level external data, thereby enabling the integration of both data types to effectively synthesize external information.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.3,"publicationDate":"2026-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149247456","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

Adaptation of Bayesian decision analysis for clinical trial design to binary outcomes: illustration for Ebola virus disease treatment. 临床试验设计贝叶斯决策分析对二元结果的适应：埃博拉病毒病治疗的例证。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-15 DOI: 10.1080/10543406.2026.2741423

Drifa Belhadi, Joonhyuk Cho, Pauline Manchon, Denis Malvy, France Mentré, Andrew W Lo, Cédric Laouénan

{"title":"Adaptation of Bayesian decision analysis for clinical trial design to binary outcomes: illustration for Ebola virus disease treatment.","authors":"Drifa Belhadi, Joonhyuk Cho, Pauline Manchon, Denis Malvy, France Mentré, Andrew W Lo, Cédric Laouénan","doi":"10.1080/10543406.2026.2741423","DOIUrl":"https://doi.org/10.1080/10543406.2026.2741423","url":null,"abstract":"When designing trials for high-mortality diseases with limited therapies, the conventional 5% type I error rate (<math><mi>α</mi></math>) may be unsuitable. A higher <math><mi>α</mi></math> could benefit seriously ill patients lacking therapeutic alternatives. Bayesian decision analysis (BDA) integrates multiple health consequences into trial design. We extend BDA for trials with binary outcomes to calculate optimal sample sizes (n) and α in an Ebola outbreak context. We consider fixed, two-arm randomized trials with binary outcomes and two types of clinical trial losses: post-trial loss, for not approving an effective treatment or approving an ineffective one; in-trial loss, for not administrating an effective treatment to control-arm patients or for administrating an ineffective treatment to experimental-arm patients. The model incorporates treatment side effects and disease severity into a single loss function, from which BDA-optimal n and α are derived. Using mortality as the outcome, we modeled various epidemic scenarios by varying target population size, mortality rate, and treatment efficacy. In most cases, BDA-optimal α exceeded the conventional one-sided 2.5% and sample sizes are smaller. Simulations comparing BDA-optimized two-arm trials (fixed/sequential) to conventional designs (two-arm/single-arm, fixed/sequential) across outbreak scenarios show comparable statistical power across designs, except when sample size assumptions are incorrect, or when trials started after the outbreak peak; in these situations, BDA-optimized trials show superior powers. This BDA framework provides a promising approach for designing trials with binary outcomes, particularly in high-mortality disease outbreaks, where declining case numbers and uncertain mortality and efficacy data challenge conventional methods.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.3,"publicationDate":"2026-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197967","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

Estimands for combination therapy oncology trials with competing risks and their integration with Project SignifiCanT. 具有竞争风险的肿瘤联合治疗试验的评估及其与项目的整合。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-13 DOI: 10.1080/10543406.2026.2741430

Ayon Mukherjee, Satrajit Roychoudhury, Atanu Bhattacharjee, James M S Wason

{"title":"Estimands for combination therapy oncology trials with competing risks and their integration with Project SignifiCanT.","authors":"Ayon Mukherjee, Satrajit Roychoudhury, Atanu Bhattacharjee, James M S Wason","doi":"10.1080/10543406.2026.2741430","DOIUrl":"https://doi.org/10.1080/10543406.2026.2741430","url":null,"abstract":"The evaluation of combination therapies in oncology increasingly demands estimand frameworks that reflect component-specific treatment strategies, complex intercurrent events (ICEs), and competing or semi-competing risk structures. Traditional hazard-based analyses often lack causal interpretability in these settings, particularly when selective discontinuation, immune-mediated toxicity, or death modifies subsequent exposure and influences event pathways. To address these limitations, we propose a unified causal-estimand framework specifically tailored for combination oncology trials. Grounded in potential-outcomes theory and aligned with ICH E9 (R1), the framework (i) defines regimen-level, component-specific, and pathway-specific estimands; (ii) formally maps these estimands to ICE strategies - treatment policy, hypothetical, composite, principal stratum, and while-on-treatment; (iii) evaluates the compatibility of commonly used statistical approaches, including cause-specific hazards, subdistribution hazards, and multi-state models, with these estimands; and (iv) integrates these elements with the regulatory objectives of the FDA's Project SignifiCanT, with particular emphasis on mechanistic interpretability, benefit-risk assessment, and transparent justification of analytical choices. By expressing estimands as contrasts of counterfactual risk or state-occupation functions - rather than relying solely on model-based hazard ratios - the framework enhances clinical relevance, supports attribution of component-specific effects, and facilitates cross-trial comparability. This work provides a principled foundation for designing, analysing, and reporting combination oncology trials, thereby advancing both methodological rigour and regulatory clarity.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-24"},"PeriodicalIF":1.3,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149186083","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

A comparative evaluation of Bayesian model-assisted two-stage designs for Phase I/II clinical trials. 贝叶斯模型辅助的I/II期临床试验两阶段设计的比较评价

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-11 DOI: 10.1080/10543406.2026.2741428

Hao Sun, Jerry Li

{"title":"A comparative evaluation of Bayesian model-assisted two-stage designs for Phase I/II clinical trials.","authors":"Hao Sun, Jerry Li","doi":"10.1080/10543406.2026.2741428","DOIUrl":"https://doi.org/10.1080/10543406.2026.2741428","url":null,"abstract":"The primary goal of a two-stage Phase I/II trial is to identify the optimal dose for the following large-scale Phase III trial. Conventional Phase I designs focus on the maximum tolerated dose (MTD), assuming that efficacy increases monotonically with dose. However, this assumption may not hold for novel therapies such as immunotherapies and targeted agents, where efficacy can plateau or decline at higher doses. In such cases, the optimal biological dose (OBD)-finding designs are more appropriate. In practice, several recommended Phase II doses (RP2Ds) are typically selected after Phase I for further evaluation in Phase II, where dose optimization designs assess toxicity and efficacy outcomes to support \"go/no-go\" decisions. The final recommended Phase III dose (RP3D) is then chosen from the RP2Ds meeting these criteria. We compared fifteen Bayesian model-assisted two-stage designs, combining five designs (BOIN, TITE-BOIN, BF-BOIN, BOIN12, and TITE-BOIN12) for dose finding with three designs (TS, BOP2, and TOP) for dose optimization, through extensive simulations under fixed and random scenarios, with and without the existence of the OBD. Our results show that BOIN12 and TITE-BOIN12 achieve better overdose control and RP2D selection, while BOP2 and TOP consistently outperform TS in RP3D selection.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.3,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149167694","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

Borrowing-dependent estimands in external-control trials: Identification, efficiency, and sensitivity in survival analysis. 外部控制试验中借贷依赖的估计：生存分析的识别、效率和敏感性。

IF 1.3 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2026-10-11 DOI: 10.1080/10543406.2026.2744763

Atanu Bhattacharjee, Ayon Mukherjee

{"title":"Borrowing-dependent estimands in external-control trials: Identification, efficiency, and sensitivity in survival analysis.","authors":"Atanu Bhattacharjee, Ayon Mukherjee","doi":"10.1080/10543406.2026.2744763","DOIUrl":"https://doi.org/10.1080/10543406.2026.2744763","url":null,"abstract":"Augmenting randomised trials with external controls is increasingly common in oncology and rare diseases. This paper reviews the methodology of externally controlled trials - estimands, identification assumptions, estimation, efficiency and sensitivity analysis - for right-censored survival outcomes, and organises it around a single question: what quantity does a borrowing analysis actually deliver? We distinguish the intended estimand, fixed in advance by the clinical question and the target population, from the borrowing-dependent estimand, the quantity to which a given borrowing rule is consistent. The two coincide under source-conditional exchangeability and separate otherwise, so that the borrowing-dependent estimand names, and makes estimable, a discrepancy that is otherwise reported only as unquantified bias. Within a nonparametric potential-outcomes framework we (i) define borrowing-weighted causal contrasts indexed by a weighting kernel and set out when such a contrast is, and is not, a scientifically meaningful target in its own right; (ii) establish nonparametric identification and derive the efficient influence function; (iii) construct a doubly robust, locally efficient estimator with <math><msqrt><mi>n</mi></msqrt></math>-consistency; (iv) introduce a sensitivity-analysis framework yielding closed-form bias bounds, E-values, and tipping points; and (v) characterise the sensitivity of the delivered estimand to the pre-specified borrowing rule via Fréchet differentiability. Simulations across 12 factorial scenarios confirm up to 49% RMSE reduction under compatibility, show that the estimators remain unbiased for the borrowing-dependent estimand when compatibility fails, and validate the sensitivity bounds. A worked example in metastatic colorectal cancer illustrates the complete analytical workflow.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-28"},"PeriodicalIF":1.3,"publicationDate":"2026-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149169359","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