Journal of Biopharmaceutical Statistics最新文献_第7页

Regional consistency assessment in multiregional clinical trials. 多区域临床试验中的区域一致性评估。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-04-01 DOI: 10.1080/10543406.2024.2330214

Gang Li, Hui Quan, Yining Wang

{"title":"Regional consistency assessment in multiregional clinical trials.","authors":"Gang Li, Hui Quan, Yining Wang","doi":"10.1080/10543406.2024.2330214","DOIUrl":"10.1080/10543406.2024.2330214","url":null,"abstract":"Multiregional clinical trials (MRCTs) have become a favored strategy for new drug development. The accurate evaluation of treatment effects across different regions is crucial for interpreting the results of MRCTs. Consistency between regional and overall results ensures the extrapolability of the overall conclusions to individual regions. While numerous statistical methods have been proposed for consistency assessment, a notable proportion necessitate a substantial escalation in sample size, particularly in scenarios involving more than four regions within MRCTs. This, paradoxically, undermines the fundamental intent of MRCTs. In addition, standardized statistical criteria for concluding consistency are yet to be established. In this paper, we develop further consistency assessment approaches in the framework of two multivariate likelihood ratio test-based methods, namely mLRTa and mLRTb, wherein consistency is cast as the alternative and null hypotheses. Notably, our exploration unveils that qualitative methods such as the funnel approach and PMDA methods are special instances of mLRTa. Furthermore, our work underscores that these three qualitative methodologies roughly share the same level of assurance probability (AP). Intriguingly, when the number of regions in an MRCT surpasses five, even when the overall sample size guarantees a power of 90% or more and the true treatment effects remain uniform across regions, the AP remains below the 70% mark. Drawing from our meticulous examination of operational attributes, we recommend mLRTa with positive treatment effects in all regions in the alternative hypothesis with significance level 0.5 or mLRTb with all regional treatment effects being equal in the null and significance level of 0.2.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"973-985"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337726","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

Empower clinical development by harnessing data from diverse sources: methodology, applications and regulatory perspectives. 通过利用不同来源的数据：方法、应用和监管视角，增强临床开发能力。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-04-01 DOI: 10.1080/10543406.2024.2333529

Shibing Deng, Shein-Chung Chow

引用次数: 0

Estimating treatment effect in randomized trial after control to treatment crossover using external controls. 利用外部对照，估算随机试验中对照与治疗交叉后的治疗效果。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-04-01 DOI: 10.1080/10543406.2024.2330209

Xiner Zhou, Herbert Pang, Christiana Drake, Hans Ulrich Burger, Jiawen Zhu

{"title":"Estimating treatment effect in randomized trial after control to treatment crossover using external controls.","authors":"Xiner Zhou, Herbert Pang, Christiana Drake, Hans Ulrich Burger, Jiawen Zhu","doi":"10.1080/10543406.2024.2330209","DOIUrl":"10.1080/10543406.2024.2330209","url":null,"abstract":"In clinical trials, it is common to design a study that permits the administration of an experimental treatment to participants in the placebo or standard of care group post primary endpoint. This is often seen in the open-label extension phase of a phase III, pivotal study of the new medicine, where the focus is on assessing long-term safety and efficacy. With the availability of external controls, proper estimation and inference of long-term treatment effect during the open-label extension phase in the absence of placebo-controlled patients are now feasible. Within the framework of causal inference, we propose several difference-in-differences (DID) type methods and a synthetic control method (SCM) for the combination of randomized controlled trials and external controls. Our realistic simulation studies demonstrate the desirable performance of the proposed estimators in a variety of practical scenarios. In particular, DID methods outperform SCM and are the recommended methods of choice. An empirical application of the methods is demonstrated through a phase III clinical trial in rare disease.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"893-921"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337724","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

List of Reviewers for Journal of Biopharmaceutical Statistics, Volume 34. 生物制药统计学杂志》第 34 卷审稿人名单。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-11-28 DOI: 10.1080/10543406.2024.2419264

引用次数: 0

FDA experiences with a centralized statistical monitoring tool. 美国食品和药物管理局使用中央统计监测工具的经验。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-03-29 DOI: 10.1080/10543406.2024.2330210

Xiaofeng Tina Wang, Paul Schuette, Matilde Kam

{"title":"FDA experiences with a centralized statistical monitoring tool.","authors":"Xiaofeng Tina Wang, Paul Schuette, Matilde Kam","doi":"10.1080/10543406.2024.2330210","DOIUrl":"10.1080/10543406.2024.2330210","url":null,"abstract":"The U.S. Food and Drug Administration (FDA) has broadly supported quality by design initiatives for clinical trials - including monitoring and data validation - by releasing two related guidance documents (FDA 2013 and 2019). Centralized statistical monitoring (CSM) can be a component of a quality by design process. In this article, we describe our experience with a CSM platform as part of a Cooperative Research and Development Agreement between CluePoints and FDA. This agreement's approach to CSM is based on many statistical tests performed on all relevant subject-level data submitted to identify outlying sites. An overall data inconsistency score is calculated to assess the inconsistency of data from one site compared to data from all sites. Sites are ranked by the data inconsistency score (<math><mo>-</mo><mrow><mrow><msub><mo>log</mo><mrow><mn>10</mn></mrow></msub></mrow></mrow><mfenced><mi>p</mi></mfenced><mo>,</mo></math>where <math><mi>p</mi></math> is an aggregated p-value). Results from a deidentified trial demonstrate the typical data anomaly findings through Statistical Monitoring Applied to Research Trials analyses. Sensitivity analyses were performed after excluding laboratory data and questionnaire data. Graphics from deidentified subject-level trial data illustrate abnormal data patterns. The analyses were performed by site, country/region, and patient separately. Key risk indicator analyses were conducted for the selected endpoints. Potential data anomalies and their possible causes are discussed. This data-driven approach can be effective and efficient in selecting sites that exhibit data anomalies and provides insights to statistical reviewers for conducting sensitivity analyses, subgroup analyses, and site by treatment effect explorations. Messy data, data failing to conform to standards, and other disruptions (e.g. the COVID-19 pandemic) can pose challenges.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"986-992"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319888","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

Assessing clinical response in early oncology development with a predictive biomarker. 利用预测性生物标记物评估早期肿瘤开发中的临床反应。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-03-22 DOI: 10.1080/10543406.2024.2330207

Shibing Deng, Feng Liu, Jadwiga Bienkowska

{"title":"Assessing clinical response in early oncology development with a predictive biomarker.","authors":"Shibing Deng, Feng Liu, Jadwiga Bienkowska","doi":"10.1080/10543406.2024.2330207","DOIUrl":"10.1080/10543406.2024.2330207","url":null,"abstract":"In early oncology clinical trials there is often limited data for biomarkers and their association with response to treatment. Thus, it is challenging to decide whether a biomarker should be used for patient selection and enrollment. Most evidence about any potential predictive biomarker comes from preclinical research and, sometimes, clinical observations. How to translate the preclinical predictive biomarker data to clinical study remains an active field of research. Here, we propose a method to incorporate existing knowledge about a predictive biomarker - its prevalence, association with response and the performance of the assay used to measure the biomarker - to estimate the response rate in a clinical study designed with or without using the predictive biomarker. Importantly, we quantify the uncertainty associated with the biomarker and its predictability in a probabilistic model. This model estimates the distribution of the clinical response when a predictive biomarker is used to select patients and compares it to unselected cohort. We applied this method to two real world cases of approved biomarker-guided therapies to demonstrate its utility and potential value. This approach helps to make a data-driven decision whether to select patients with a predictive biomarker in early oncology clinical development.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1033-1044"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140190432","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

On the use of RWD in support of regulatory submission in drug development. 使用 RWD 支持药物开发的监管申请。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-03-19 DOI: 10.1080/10543406.2024.2330213

Shein-Chung Chow, Peijin Wang

{"title":"On the use of RWD in support of regulatory submission in drug development.","authors":"Shein-Chung Chow, Peijin Wang","doi":"10.1080/10543406.2024.2330213","DOIUrl":"10.1080/10543406.2024.2330213","url":null,"abstract":"For the approval of a drug product, the United States Food and Drug Administration requires substantial evidence (SE) regarding effectiveness and safety of the test drug to be provided. In recent years, the use of real-world data in support of regulatory submission of pharmaceutical development has received much attention, and real-world evidence (RWE) is treated as complementary to SE by evaluating the real-world performance of the test treatment. In this article, we start by summarizing current regulatory perspectives on drug evaluation and some potential challenges in using RWE. To test for superiority in co-primary endpoints, a two-stage hybrid RCT/RWS adaptive design that combines randomized control trial for providing SE and real-world study for generating RWE is proposed. We use superiority in effectiveness and non-inferiority in safety as an example to illustrate how to implement this design. Numerical studies have shown that the proposed design has merits in reducing the required sample size compared with traditional co-primary endpoint tests while maintaining statistical power and controlling type I error inflation. The proposed design can be implemented in drug development considering co-primary endpoints, especially for oncology and rare disease drug development.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"777-804"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140159544","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

Covariate adjusted meta-analytic predictive (CA-MAP) prior for historical borrowing using patient-level data. 利用患者层面的数据对历史借贷进行共变量调整元分析预测（CA-MAP）先验。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-04-01 DOI: 10.1080/10543406.2024.2330206

Bradley Hupf, Yunlong Yang, Ryan Gryder, Veronica Bunn, Jianchang Lin

{"title":"Covariate adjusted meta-analytic predictive (CA-MAP) prior for historical borrowing using patient-level data.","authors":"Bradley Hupf, Yunlong Yang, Ryan Gryder, Veronica Bunn, Jianchang Lin","doi":"10.1080/10543406.2024.2330206","DOIUrl":"10.1080/10543406.2024.2330206","url":null,"abstract":"Utilization of historical data is increasingly common for gaining efficiency in the drug development and decision-making processes. The underlying issue of between-trial heterogeneity in clinical trials is a barrier in making these methods standard practice in the pharmaceutical industry. Common methods for historical borrowing discount the borrowed information based on the similarity between outcomes in the historical and current data. However, individual clinical trials and their outcomes are intrinsically heterogenous due to differences in study design, patient characteristics, and changes in standard of care. Additionally, differences in covariate distributions can produce inconsistencies in clinical outcome data between historical and current data when there may be a consistent covariate effect. In such scenario, borrowing historical data is still advantageous even though the population level outcome summaries are different. In this paper, we propose a covariate adjusted meta-analytic-predictive (CA-MAP) prior for historical control borrowing. A MAP prior is assigned to each covariate effect, allowing the amount of borrowing to be determined by the consistency of the covariate effects across the current and historical data. This approach integrates between-trial heterogeneity with covariate level heterogeneity to tune the amount of information borrowed. Our method is unique as it directly models the covariate effects instead of using the covariates to select a similar population to borrow from. In summary, our proposed patient-level extension of the MAP prior allows for the amount of historical control borrowing to depend on the similarity of covariate effects rather than similarity in clinical outcomes.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"944-952"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337722","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

Adaptively leverage multiple real-world data sources for treatment effect estimation based on similarity. 基于相似性，自适应地利用多个真实世界数据源进行治疗效果估算。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-04-01 DOI: 10.1080/10543406.2024.2330202

Meihua Long, Jiali Song, Zhiwei Rong, Lan Mi, Yuqin Song, Yan Hou

{"title":"Adaptively leverage multiple real-world data sources for treatment effect estimation based on similarity.","authors":"Meihua Long, Jiali Song, Zhiwei Rong, Lan Mi, Yuqin Song, Yan Hou","doi":"10.1080/10543406.2024.2330202","DOIUrl":"10.1080/10543406.2024.2330202","url":null,"abstract":"The incorporation of real-world data (RWD) into medical product development and evaluation has exhibited consistent growth. However, there is no universally adopted method of how much information to borrow from external data. This paper proposes a study design methodology called Tree-based Monte Carlo (TMC) that dynamically integrates patients from various RWD sources to calculate the treatment effect based on the similarity between clinical trial and RWD. Initially, a propensity score is developed to gauge the resemblance between clinical trial data and each real-world dataset. Utilizing this similarity metric, we construct a hierarchical clustering tree that delineates varying degrees of similarity between each RWD source and the clinical trial data. Ultimately, a Gaussian process methodology is employed across this hierarchical clustering framework to synthesize the projected treatment effects of the external group. Simulation result shows that our clustering tree could successfully identify similarity. Data sources exhibiting greater similarity with clinical trial are accorded higher weights in treatment estimation process, while less congruent sources receive comparatively lower emphasis. Compared with another Bayesian method, meta-analytic predictive prior (MAP), our proposed method's estimator is closer to the true value and has smaller bias.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"853-863"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140337721","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

Correction. 更正。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-11-28 DOI: 10.1080/10543406.2024.2428565

引用次数: 0