Journal of Biopharmaceutical Statistics最新文献_第2页

Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis. 在重复测量分析的混合模型中，无需假定正交性即可进行推理的小样本调整。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-29 DOI: 10.1080/10543406.2024.2420632

Kazushi Maruo, Ryota Ishii, Yusuke Yamaguchi, Tomohiro Ohigashi, Masahiko Gosho

{"title":"Small sample adjustment for inference without assuming orthogonality in a mixed model for repeated measures analysis.","authors":"Kazushi Maruo, Ryota Ishii, Yusuke Yamaguchi, Tomohiro Ohigashi, Masahiko Gosho","doi":"10.1080/10543406.2024.2420632","DOIUrl":"https://doi.org/10.1080/10543406.2024.2420632","url":null,"abstract":"The mixed model for repeated measures (MMRM) analysis is sometimes used as a primary statistical analysis for a longitudinal randomized clinical trial. When the MMRM analysis is implemented in ordinary statistical software, the standard error of the treatment effect is estimated by assuming orthogonality between the fixed effects and covariance parameters, based on the characteristics of the normal distribution. However, orthogonality does not hold unless the normality assumption of the error distribution holds, and/or the missing data are derived from the missing completely at random structure. Therefore, assuming orthogonality in the MMRM analysis is not preferable. However, without the assumption of orthogonality, the small-sample bias in the standard error of the treatment effect is significant. Nonetheless, there is no method to improve small-sample performance. Furthermore, there is no software that can easily implement inferences on treatment effects without assuming orthogonality. Hence, we propose two small-sample adjustment methods inflating standard errors that are reasonable in ideal situations and achieve empirical conservatism even in general situations. We also provide an R package to implement these inference processes. The simulation results show that one of the proposed small-sample adjustment methods performs particularly well in terms of underestimation bias of standard errors; consequently, the proposed method is recommended. When using the MMRM analysis, our proposed method is recommended if the sample size is not large and between-group heteroscedasticity is expected.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2024-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142549003","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

Leveraging real-world data to conduct externally controlled trial for rare diseases with count-type endpoints: utilizing multiple entries - a simulation study. 利用真实世界的数据开展以计数型终点为对象的罕见病外部对照试验：利用多个条目--一项模拟研究。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-27 DOI: 10.1080/10543406.2024.2420644

Tianyu Sun, Eileen Liao, Nan Shao, Junxiang Luo

{"title":"Leveraging real-world data to conduct externally controlled trial for rare diseases with count-type endpoints: utilizing multiple entries - a simulation study.","authors":"Tianyu Sun, Eileen Liao, Nan Shao, Junxiang Luo","doi":"10.1080/10543406.2024.2420644","DOIUrl":"https://doi.org/10.1080/10543406.2024.2420644","url":null,"abstract":"Conducting randomized controlled trials for medications targeting rare diseases presents significant challenges, due to the scarcity of participants and ethical considerations. Under such circumstances, leveraging real-world data (RWD) to generate supporting evidence may be accepted by the regulatory agency. Constructing an external control arm (ECA) from RWD for a single-arm trial has been conducted occasionally. A complication in this design is that patients from RWD may be eligible at multiple time points. Most studies approach this by selecting one time point as the index date for ECA patients. Here, we propose a novel design for externally controlled trials that permits the inclusion of ECA patients at various entry points. Accompanying this design, we make recommendations for statistical methods to account for measured confounders, limited sample size, within-subject correlation, and potential overdispersion inherent in count data. Furthermore, we present an idea for the blinding process for this type of study. We have conducted a series of simulations to assess the performance of the design and statistical methods in terms of bias, type I error, and efficiency, as compared to the approach of selecting only one entry per ECA patient. The study and parameter setup were based on a hypothetical case inspired by a rare disease study. The results indicate that allowing multiple entries for ECA patients can lead to enhanced performance in many aspects. It provides a controlled type I error, robustness against certain model misspecifications, and a moderate power improvement compared with selecting a single entry per ECA patient.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142513228","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

Issues in cox proportional hazards model with unequal randomization. 采用不等随机化的考克斯比例危险模型的问题。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-24 DOI: 10.1080/10543406.2024.2418139

Hongfei Li, Qian H Li, Chuan Tian, Kevin Hou

引用次数: 0

On approximate equality of Z-values of the statistical tests for win statistics (win ratio, win odds, and net benefit). 关于胜率统计（胜率、胜算和净收益）的 Z 值近似相等的统计检验。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-10-08 DOI: 10.1080/10543406.2024.2374857

Gaohong Dong, Ying Cui, Margaret Gamalo-Siebers, Ran Liao, Dacheng Liu, David C Hoaglin, Ying Lu

{"title":"On approximate equality of Z-values of the statistical tests for win statistics (win ratio, win odds, and net benefit).","authors":"Gaohong Dong, Ying Cui, Margaret Gamalo-Siebers, Ran Liao, Dacheng Liu, David C Hoaglin, Ying Lu","doi":"10.1080/10543406.2024.2374857","DOIUrl":"https://doi.org/10.1080/10543406.2024.2374857","url":null,"abstract":"Dong et al. (2023) showed that the win statistics (win ratio, win odds, and net benefit) can complement each another to demonstrate the strength of treatment effects in randomized trials with prioritized multiple outcomes. This result was built on the connections among the point and variance estimates of the three statistics, and the approximate equality of Z-values in their statistical tests. However, the impact of this approximation was not clear. This Discussion refines this approach and shows that the approximate equality of Z-values for the win statistics holds more generally. Thus, the three win statistics consistently yield closely similar p-values. In addition, our simulations show an example that the naive approach without adjustment for censoring bias may produce a completely opposite conclusion from the true results, whereas the IPCW (inverse-probability-of-censoring weighting) approach can effectively adjust the win statistics to the corresponding true values (i.e. IPCW-adjusted win statistics are unbiased estimators of treatment effect).","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-8"},"PeriodicalIF":1.2,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142395406","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

Robust safety monitoring and signal detection using alternatives to the standard poisson distribution. 使用标准泊松分布的替代品进行稳健的安全监控和信号检测。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-09-11 DOI: 10.1080/10543406.2024.2395532

Benjamin Duncan

{"title":"Robust safety monitoring and signal detection using alternatives to the standard poisson distribution.","authors":"Benjamin Duncan","doi":"10.1080/10543406.2024.2395532","DOIUrl":"https://doi.org/10.1080/10543406.2024.2395532","url":null,"abstract":"Proper and timely characterization of the safety profile of a pharmaceutical product under development is imperative for assessing the overall benefit-risk relationship of the product and for making key development decisions. For ongoing clinical development, a comprehensive and robust safety monitoring and safety signal detection program which is based upon quantitative statistical reasoning is critical. Methods presented here can be applied to safety signal detection and periodic safety monitoring. Various statistical properties, distributions, and models, all utilizing a Bayesian framework are considered and further examined in order to identify robust methods applicable to a broad set of scenarios and situations. Methods developed for incidence counts (including those with under-dispersed distributions) with variable time-at-risk and with underlying constant or non-constant hazard rates, are proposed and compared to traditional methods designed to assess adverse event incidence rates or binomial incidence proportions (which assume an underlying constant hazard rate and subsequent Poisson distribution for modeling event counts).","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":"1 1","pages":"1-18"},"PeriodicalIF":1.1,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142178780","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