Journal of Biopharmaceutical Statistics最新文献_第7页

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

Analysis of innovative two-stage seamless adaptive design with different endpoints and population shift. 分析具有不同终点和人口转移的创新型两阶段无缝适应性设计。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-03-21 DOI: 10.1080/10543406.2024.2330204

Weijia Mai, Shein-Chung Chow

{"title":"Analysis of innovative two-stage seamless adaptive design with different endpoints and population shift.","authors":"Weijia Mai, Shein-Chung Chow","doi":"10.1080/10543406.2024.2330204","DOIUrl":"10.1080/10543406.2024.2330204","url":null,"abstract":"In recent years, clinical trials utilizing a two-stage seamless adaptive trial design have become very popular in drug development. A typical example is a phase 2/3 adaptive trial design, which consists of two stages. As an example, stage 1 is for a phase 2 dose-finding study and stage 2 is for a phase 3 efficacy confirmation study. Depending upon whether or not the target patient population, study objectives, and study endpoints are the same at different stages, Chow (2020) classified two-stage seamless adaptive design into eight categories. In practice, standard statistical methods for group sequential design with one planned interim analysis are often wrongly directly applied for data analysis. In this article, following similar ideas proposed by Chow and Lin (2015) and Chow (2020), a statistical method for the analysis of a two-stage seamless adaptive trial design with different study endpoints and shifted target patient population is discussed under the fundamental assumption that study endpoints have a known relationship. The proposed analysis method should be useful in both clinical trials with protocol amendments and clinical trials with the existence of disease progression utilizing a two-stage seamless adaptive trial design.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"993-1006"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186334","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

Flexible seamless 2-in-1 design with sample size adaptation. 灵活无缝的二合一设计，可适应样品大小。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-03-29 DOI: 10.1080/10543406.2024.2330211

Runjia Li, Liwen Wu, Rachael Liu, Jianchang Lin

{"title":"Flexible seamless 2-in-1 design with sample size adaptation.","authors":"Runjia Li, Liwen Wu, Rachael Liu, Jianchang Lin","doi":"10.1080/10543406.2024.2330211","DOIUrl":"10.1080/10543406.2024.2330211","url":null,"abstract":"The 2-in-1 design is becoming popular in oncology drug development, with the flexibility in using different endpoints at different decision time. Based on the observed interim data, sponsors can choose to seamlessly advance a small phase 2 trial to a full-scale confirmatory phase 3 trial with a pre-determined maximum sample size or remain in a phase 2 trial. While this approach may increase efficiency in drug development, it is rigid and requires a pre-specified fixed sample size. In this paper, we propose a flexible 2-in-1 design with sample size adaptation, while retaining the advantage of allowing an intermediate endpoint for interim decision-making. The proposed design reflects the needs of the recent FDA's Project FrontRunner initiative, which encourages the use of an earlier surrogate endpoint to potentially support accelerated approval with conversion to standard approval with long-term endpoints from the same randomized study. Additionally, we identify the interim decision cut-off to allow a conventional test procedure at the final analysis. Extensive simulation studies showed that the proposed design requires much a smaller sample size and shorter timeline than the simple 2-in-1 design, while achieving similar power. We present a case study in multiple myeloma to demonstrate the benefits of the proposed design.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1007-1025"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140319889","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

Transporting survival of an HIV clinical trial to the external target populations. 将艾滋病临床试验的存活率传递给外部目标人群。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-01 Epub Date: 2024-03-23 DOI: 10.1080/10543406.2024.2330216

Dasom Lee, Chenyin Gao, Sujit Ghosh, Shu Yang

{"title":"Transporting survival of an HIV clinical trial to the external target populations.","authors":"Dasom Lee, Chenyin Gao, Sujit Ghosh, Shu Yang","doi":"10.1080/10543406.2024.2330216","DOIUrl":"10.1080/10543406.2024.2330216","url":null,"abstract":"Due to the heterogeneity of the randomized controlled trial (RCT) and external target populations, the estimated treatment effect from the RCT is not directly applicable to the target population. For example, the patient characteristics of the ACTG 175 HIV trial are significantly different from that of the three external target populations of interest: US early-stage HIV patients, Thailand HIV patients, and southern Ethiopia HIV patients. This paper considers several methods to transport the treatment effect from the ACTG 175 HIV trial to the target populations beyond the trial population. Most transport methods focus on continuous and binary outcomes; on the contrary, we derive and discuss several transport methods for survival outcomes: an outcome regression method based on a Cox proportional hazard (PH) model, an inverse probability weighting method based on the models for treatment assignment, sampling score, and censoring, and a doubly robust method that combines both methods, called the augmented calibration weighting (ACW) method. However, as the PH assumption was found to be incorrect for the ACTG 175 trial, the methods that depend on the PH assumption may lead to the biased quantification of the treatment effect. To account for the violation of the PH assumption, we extend the ACW method with the linear spline-based hazard regression model that does not require the PH assumption. Applying the aforementioned methods for transportability, we explore the effect of PH assumption, or the violation thereof, on transporting the survival results from the ACTG 175 trial to various external populations.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"922-943"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140195103","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 the hierarchical beta-binomial model as a basic information sharing tool in basket trials. 评估作为篮子试验基本信息共享工具的分层 beta-二叉模型。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-09-26 DOI: 10.1080/10543406.2024.2399203

Moritz Pohl, Lukas D Sauer, Meinhard Kieser

{"title":"Assessing the hierarchical beta-binomial model as a basic information sharing tool in basket trials.","authors":"Moritz Pohl, Lukas D Sauer, Meinhard Kieser","doi":"10.1080/10543406.2024.2399203","DOIUrl":"https://doi.org/10.1080/10543406.2024.2399203","url":null,"abstract":"The majority of statistical methods to share information in basket trials are based on a Bayesian hierarchical model with a common normal distribution for the logit-transformed response rates. The methods are of varying complexity, yet they all use this basic model. Generally, complexity is an obstacle for the application in clinical trials and that includes the use of the logit-transformation. The transformation complicates the model and impedes a direct interpretation of the hyperparameters. On the other hand, there exist basket trial designs which directly work on the probability scale of the response rate which facilitates the understanding of the model for many stakeholders. In order to reduce unnecessary complexity, we considered using a hierarchical beta-binomial model instead of the transformed models. This article investigates whether this approach is a practicable alternative to the commonly applied sharing tools based on a logit-transformation of the response rates. For this purpose, we performed a systematic comparison of the two models, starting with the distributional assumptions for the response rates, continuing with the Bayesian behavior together with binomial data in an independent setting and ended with a simulation study for the hierarchical model under various data and prior scenarios. All Bayesian comparisons require equal starting points, wherefore we propose a calibration procedure to choose similar priors for the models. The evaluation of the sharing property additionally required an evaluation measure for simulation results, which we derived in this work. The conclusion of the comparison is that the hierarchical beta-binomial model is a feasible alternative basic model to share information in basket trials.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-33"},"PeriodicalIF":1.2,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142332567","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

Non-constant mean relative potency for antibody-dependent cellular cytotoxicity assays. 抗体依赖性细胞毒性试验的非恒定平均相对效力。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-09-22 DOI: 10.1080/10543406.2024.2403435

Paul Faya, Tianhui Zhang, Wendy Walton, Steven Novick

{"title":"Non-constant mean relative potency for antibody-dependent cellular cytotoxicity assays.","authors":"Paul Faya, Tianhui Zhang, Wendy Walton, Steven Novick","doi":"10.1080/10543406.2024.2403435","DOIUrl":"https://doi.org/10.1080/10543406.2024.2403435","url":null,"abstract":"Bioassays are regulated, analytical methods used to ensure proper activity (potency) of biological products at release and during long-term storage. Potency is commonly reported on a relative basis by comparing and calibrating a concentration-response curve from the test material to that of a reference standard material. The relative potency approach depends on an assumption that the two concentration-response curves exhibit similar (equivalent) shapes, except for a potency shift. In certain circumstances, however, biological factors preclude the similarity assumption, and the traditional approach becomes unworkable. The antibody-mediated cytotoxicity assay is one example where the similarity assumption does not always hold. Other examples also arise in the fields of toxicology and pharmacology. In this work, we present a non-constant mean relative potency approach which averages the relative potency across a common range of the concentration-response curves. The proposed method captures the changing nature of the relative potency into a summary statistic that can be reported for batch calibration and quality control purposes. We provide inferential methods for this statistic and summarize the results of a simulation comparing these methods across a number of non-constant relative potency scenarios and assay conditions.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-12"},"PeriodicalIF":1.2,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301313","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 analyses of multiple random change points in survival models with applications to clinical trials. 生存模型中多个随机变化点的贝叶斯分析在临床试验中的应用。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-09-22 DOI: 10.1080/10543406.2024.2395542

Jianbo Xu

{"title":"Bayesian analyses of multiple random change points in survival models with applications to clinical trials.","authors":"Jianbo Xu","doi":"10.1080/10543406.2024.2395542","DOIUrl":"https://doi.org/10.1080/10543406.2024.2395542","url":null,"abstract":"Single and multiple random change points (RCPs) in survival analysis have arisen naturally in oncology trials, where the time to hazard rate change differs from one subject to another. Recently, Xu formulated and discovered important properties of these survival models using a frequentist approach, allowing us to estimate the hazard rates, rate parameters of the exponential distributions for the RCPs, expected survival and hazard functions. However, these methods did not provide an estimation of the uncertainty or the confidence intervals for the parameters and their differences or ratios. Therefore, statistical inferences were not able to be drawn on the parameters and their comparisons. To solve this issue, this article implements a Gibbs sampler method to estimate the above parameters and the differences or ratios alongside the 100(1 <math><mo>-</mo></math> <math><mi>α</mi></math>)% highest posterior density (HPD) intervals calculated from Chen-Shao's algorithm. The estimated rate parameters from the methods in Xu serve as empirical values in the Gibbs sampler method. Thus, formal statistical inferences can now be readily drawn. Simulation studies demonstrate that the proposed methods yield robust estimates, with the samples from the marginal posterior distributions converging rapidly and exhibiting favorable behavior. The 95% HPD intervals also demonstrate excellent coverage probabilities. This proposed method has a multitude of applications in clinical trials such as efficient clinical trial design and sample size adjustment based on the estimated parameter values at interim analyses.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2024-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301310","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

Developing large language models to detect adverse drug events in posts on x. 开发大型语言模型，检测 x 上帖子中的药物不良事件。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-09-20 DOI: 10.1080/10543406.2024.2403442

Yu Deng, Yunzhao Xing, Jason Quach, Xiaotian Chen, Xiaoqiang Wu, Yafei Zhang, Charlotte Moureaud, Mengjia Yu, Yujie Zhao, Li Wang, Sheng Zhong

{"title":"Developing large language models to detect adverse drug events in posts on x.","authors":"Yu Deng, Yunzhao Xing, Jason Quach, Xiaotian Chen, Xiaoqiang Wu, Yafei Zhang, Charlotte Moureaud, Mengjia Yu, Yujie Zhao, Li Wang, Sheng Zhong","doi":"10.1080/10543406.2024.2403442","DOIUrl":"https://doi.org/10.1080/10543406.2024.2403442","url":null,"abstract":"Adverse drug events (ADEs) are one of the major causes of hospital admissions and are associated with increased morbidity and mortality. Post-marketing ADE identification is one of the most important phases of drug safety surveillance. Traditionally, data sources for post-marketing surveillance mainly come from spontaneous reporting system such as the Food and Drug Administration Adverse Event Reporting System (FAERS). Social media data such as posts on X (formerly Twitter) contain rich patient and medication information and could potentially accelerate drug surveillance research. However, ADE information in social media data is usually locked in the text, making it difficult to be employed by traditional statistical approaches. In recent years, large language models (LLMs) have shown promise in many natural language processing tasks. In this study, we developed several LLMs to perform ADE classification on X data. We fine-tuned various LLMs including BERT-base, Bio_ClinicalBERT, RoBERTa, and RoBERTa-large. We also experimented ChatGPT few-shot prompting and ChatGPT fine-tuned on the whole training data. We then evaluated the model performance based on sensitivity, specificity, negative predictive value, positive predictive value, accuracy, F1-measure, and area under the ROC curve. Our results showed that RoBERTa-large achieved the best F1-measure (0.8) among all models followed by ChatGPT fine-tuned model with F1-measure of 0.75. Our feature importance analysis based on 1200 random samples and RoBERTa-Large showed the most important features are as follows: \"withdrawals\"/\"withdrawal\", \"dry\", \"dealing\", \"mouth\", and \"paralysis\". The good model performance and clinically relevant features show the potential of LLMs in augmenting ADE detection for post-marketing drug safety surveillance.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-12"},"PeriodicalIF":1.2,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142301311","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

Multi-arm multi-stage survival trial design with arm-specific stopping rule. 多臂多阶段生存试验设计，采用特定臂停止规则。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-09-16 DOI: 10.1080/10543406.2024.2398036

Jianrong Wu, Yimei Li, Liang Zhu, Tushar Patni

引用次数: 0