Journal of Biopharmaceutical Statistics最新文献_第10页

Pharmacometrics-Enabled DOse OPtimization (PEDOOP) for seamless phase I-II trials in oncology. 用于肿瘤学 I-II 期无缝试验的药物计量学 DOse OPtimization (PEDOOP)。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-18 DOI: 10.1080/10543406.2024.2364716

Shijie Yuan, Zhanbo Huang, Jiaxin Liu, Yuan Ji

{"title":"Pharmacometrics-Enabled DOse OPtimization (PEDOOP) for seamless phase I-II trials in oncology.","authors":"Shijie Yuan, Zhanbo Huang, Jiaxin Liu, Yuan Ji","doi":"10.1080/10543406.2024.2364716","DOIUrl":"https://doi.org/10.1080/10543406.2024.2364716","url":null,"abstract":"We consider a dose-optimization design for a first-in-human oncology trial that aims to identify a suitable dose for late-phase drug development. The proposed approach, called the Pharmacometrics-Enabled DOse OPtimization (PEDOOP) design, incorporates observed patient-level pharmacokinetics (PK) measurements and latent pharmacodynamics (PD) information for trial decision-making and dose optimization. PEDOOP consists of two seamless phases. In phase I, patient-level time-course drug concentrations, derived PD effects, and the toxicity outcomes from patients are integrated into a statistical model to estimate the dose-toxicity response. A simple dose-finding design guides dose escalation in phase I. At the end of the phase I dose finding, a graduation rule is used to assess the safety and efficacy of all the doses and select those with promising efficacy and acceptable safety for a randomized comparison against a control arm in phase II. In phase II, patients are randomized to the selected doses based on a fixed or adaptive randomization ratio. At the end of phase II, an optimal biological dose (OBD) is selected for late-phase development. We conduct simulation studies to assess the PEDOOP design in comparison to an existing seamless design that also combines phases I and II in a single trial.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421880","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 comparison of Bayesian and score methods for interval estimates of positive/negative likelihood ratios in support of diagnostic device performance evaluation. 比较贝叶斯法和记分法对阳性/阴性似然比的区间估计，以支持诊断设备性能评估。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-18 DOI: 10.1080/10543406.2024.2364723

Tingting Hu, Berkman Sahiner, Nicholas Petrick, Kenny Cha, Si Wen, Gene Pennello

{"title":"A comparison of Bayesian and score methods for interval estimates of positive/negative likelihood ratios in support of diagnostic device performance evaluation.","authors":"Tingting Hu, Berkman Sahiner, Nicholas Petrick, Kenny Cha, Si Wen, Gene Pennello","doi":"10.1080/10543406.2024.2364723","DOIUrl":"https://doi.org/10.1080/10543406.2024.2364723","url":null,"abstract":"Background: Positive and negative likelihood ratios (PLR and NLR) are important metrics of accuracy for diagnostic devices with a binary output. However, the properties of Bayesian and frequentist interval estimators of PLR/NLR have not been extensively studied and compared. In this study, we explore the potential use of the Bayesian method for interval estimation of PLR/NLR, and, more broadly, for interval estimation of the ratio of two independent proportions.Methods: We develop a Bayesian-based approach for interval estimation of PLR/NLR for use as a part of a diagnostic device performance evaluation. Our approach is applicable to a broader setting for interval estimation of any ratio of two independent proportions. We compare score and Bayesian interval estimators for the ratio of two proportions in terms of the coverage probability (CP) and expected interval width (EW) via extensive experiments and applications to two case studies. A supplementary experiment was also conducted to assess the performance of the proposed exact Bayesian method under different priors.Results: Our experimental results show that the overall mean CP for Bayesian interval estimation is consistent with that for the score method (0.950 vs. 0.952), and the overall mean EW for Bayesian is shorter than that for score method (15.929 vs. 19.724). Application to two case studies showed that the intervals estimated using the Bayesian and frequentist approaches are very similar.Discussion: Our numerical results indicate that the proposed Bayesian approach has a comparable CP performance with the score method while yielding higher precision (i.e. a shorter EW).","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-19"},"PeriodicalIF":1.1,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421852","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

Clustering plasma concentration-time curves: applications of unsupervised learning in pharmacogenomics. 血浆浓度-时间曲线聚类：无监督学习在药物基因组学中的应用。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-18 DOI: 10.1080/10543406.2024.2365389

Jackson P Lautier, Stella Grosser, Jessica Kim, Hyewon Kim, Junghi Kim

{"title":"Clustering plasma concentration-time curves: applications of unsupervised learning in pharmacogenomics.","authors":"Jackson P Lautier, Stella Grosser, Jessica Kim, Hyewon Kim, Junghi Kim","doi":"10.1080/10543406.2024.2365389","DOIUrl":"10.1080/10543406.2024.2365389","url":null,"abstract":"Pharmaceutical researchers are continually searching for techniques to improve both drug development processes and patient outcomes. An area of recent interest is the potential for machine learning (ML) applications within pharmacology. One such application not yet given close study is the unsupervised clustering of plasma concentration-time curves, hereafter, pharmacokinetic (PK) curves. In this paper, we present our findings on how to cluster PK curves by their similarity. Specifically, we find clustering to be effective at identifying similar-shaped PK curves and informative for understanding patterns within each cluster of PK curves. Because PK curves are time series data objects, our approach utilizes the extensive body of research related to the clustering of time series data as a starting point. As such, we examine many dissimilarity measures between time series data objects to find those most suitable for PK curves. We identify Euclidean distance as generally most appropriate for clustering PK curves, and we further show that dynamic time warping, Fréchet, and structure-based measures of dissimilarity like correlation may produce unexpected results. As an illustration, we apply these methods in a case study with 250 PK curves used in a previous pharmacogenomic study. Our case study finds that an unsupervised ML clustering with Euclidean distance, without any subject genetic information, is able to independently validate the same conclusions as the reference pharmacogenomic results. To our knowledge, this is the first such demonstration. Further, the case study demonstrates how the clustering of PK curves may generate insights that could be difficult to perceive solely with population level summary statistics of PK metrics.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-19"},"PeriodicalIF":1.2,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141421878","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

Drug safety assessment by machine learning models. 用机器学习模型评估药物安全性。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-18 DOI: 10.1080/10543406.2024.2365976

Nan Miles Xi, Dalong Patrick Huang

引用次数: 0

Meta-analysis application to hERG safety evaluation in clinical trials. 将 Meta 分析应用于临床试验中的 hERG 安全性评估。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-14 DOI: 10.1080/10543406.2024.2365972

Xutong Zhao, Jing Sun, Dalong Huang

{"title":"Meta-analysis application to hERG safety evaluation in clinical trials.","authors":"Xutong Zhao, Jing Sun, Dalong Huang","doi":"10.1080/10543406.2024.2365972","DOIUrl":"10.1080/10543406.2024.2365972","url":null,"abstract":"One objective of meta-analysis, which synthesizes evidence across multiple studies, is to assess the consistency and investigate the heterogeneity across studies. In this project, we performed a meta-analysis on moxifloxacin (positive control in QT assessment studies) data to characterize the exposure-response relationship and determine the safety margin associated with 10-msec QTc effects for moxifloxacin based on 26 thorough QT studies submitted to the FDA. Multiple meta-analysis methods were used (including two novel methods) to evaluate the exposure-response relationship and estimate the critical concentration and the corresponding confidence interval of moxifloxacin associated with a 10-msec QTc effect based on the concentration-QTc models. These meta-analysis methods (aggregate data vs. individual participant data; fixed effect vs. random effect) were compared in terms of their precision and robustness. With the selected meta-analysis method, we demonstrated the homogeneity and heterogeneity of the moxifloxacin concentration-QTc relationship in studies. We also estimated the critical concentration of moxifloxacin that can be used to calculate the hERG safety margin of this drug.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141321958","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

PROpwr: a Shiny R application to analyze patient-reported outcomes data and estimate power. PROpwr：一个 Shiny R 应用程序，用于分析患者报告的结果数据并估算功率。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-13 DOI: 10.1080/10543406.2024.2365966

Jinxiang Hu, Xiaohang Mei, Sam Pepper, Yu Wang, Bo Zhang, Colin Cernik, Byron Gajewski

引用次数: 0

Optimal two-phase sampling for comparing correlated areas under the ROC curves of two screening tests in the presence of verification bias. 在存在验证偏差的情况下，比较两种筛选测试 ROC 曲线下相关区域的最佳两阶段采样。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-13 DOI: 10.1080/10543406.2024.2358803

Yougui Wu

{"title":"Optimal two-phase sampling for comparing correlated areas under the ROC curves of two screening tests in the presence of verification bias.","authors":"Yougui Wu","doi":"10.1080/10543406.2024.2358803","DOIUrl":"https://doi.org/10.1080/10543406.2024.2358803","url":null,"abstract":"The accuracy of a screening test is often measured by the area under the receiver characteristic (ROC) curve (AUC) of a screening test. Two-phase designs have been widely used in diagnostic studies for estimating one single AUC and comparing two AUCs where the screening test results are measured for a large sample (Phase one sample) while the disease status is only verified for a subset of Phase one sample (Phase two sample) by a gold standard. In this paper, we consider the optimal two-phase sampling design for comparing the performance of two ordinal screening tests in classifying disease status. Specifically, we derive an analytical variance formula for the AUC difference estimator and use it to find the optimal sampling probabilities that minimize the variance formula for the AUC difference estimator. According to the proposed optimal two-phase design, the strata with the levels of two tests far apart from each other should be over-sampled while the strata with the levels of two tests close to each other should be under-sampled. Simulation results indicate that two-phase sampling under optimal allocation (OA) achieves a substantial amount of variance reduction, compared with two-phase sampling under proportional allocation (PA). Furthermore, in comparison with a one-phase random sampling, two-phase sampling under OA or PA has a clear advantage in reducing the variance of AUC difference estimator when the variances of the two screening test results in the disease population differ greatly from their counterparts in non-disease population.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.1,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141312284","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 machine learning case study to predict rare clinical event of interest: imbalanced data, interpretability, and practical considerations. 预测罕见临床事件的机器学习案例研究：不平衡数据、可解释性和实际考虑因素。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-11 DOI: 10.1080/10543406.2024.2364722

Sheng Zhong, Jane Zhang, Jenny Jiao, Hongjian Zhu, Yunzhao Xing, Li Wang

{"title":"A machine learning case study to predict rare clinical event of interest: imbalanced data, interpretability, and practical considerations.","authors":"Sheng Zhong, Jane Zhang, Jenny Jiao, Hongjian Zhu, Yunzhao Xing, Li Wang","doi":"10.1080/10543406.2024.2364722","DOIUrl":"https://doi.org/10.1080/10543406.2024.2364722","url":null,"abstract":"Accurate prediction of a rare and clinically important event following study treatment has been crucial in drug development. For instance, the rarity of an adverse event is often commensurate with the seriousness of medical consequences, and delayed detection of the rare adverse event can pose significant or even life-threatening health risks to patients. In this machine learning case study, we demonstrate with an example originated from a real clinical trial setting how to define and solve the rare clinical event prediction problem using machine learning in pharmaceutical industry. The unique contributions of this work include the proposal of a six-step investigation framework that facilitates the communication with non-technical stakeholders and the interpretation of the model performance in terms of practical consequences in the context of patient screenings for conducting a future clinical trial. In terms of machine learning methodology, for data splitting into the training and test sets, we adapt the rare-event stratified split approach (from scikit-learn) to further account for group splitting for multiple records of a patient simultaneously. To handle imbalanced data due to rare events in model training, the cost-sensitive learning approach is employed to give more weights to the minor class and the metrics precision together with recall are used to capture prediction performance instead of the raw accuracy rate. Finally, we demonstrate how to apply the state-of-the-art SHAP values to identify important risk factors to improve model interpretability.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-14"},"PeriodicalIF":1.1,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302167","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

Investigating pharmacokinetic profiles of Centella asiatica using machine learning and PBPK modelling. 利用机器学习和 PBPK 模型研究积雪草的药代动力学特征。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-11 DOI: 10.1080/10543406.2024.2358797

Siriwan Pumkathin, Yuranan Hanlumyuang, Worawat Wattanathana, Teeraphan Laomettachit, Monrudee Liangruksa

{"title":"Investigating pharmacokinetic profiles of Centella asiatica using machine learning and PBPK modelling.","authors":"Siriwan Pumkathin, Yuranan Hanlumyuang, Worawat Wattanathana, Teeraphan Laomettachit, Monrudee Liangruksa","doi":"10.1080/10543406.2024.2358797","DOIUrl":"https://doi.org/10.1080/10543406.2024.2358797","url":null,"abstract":"Physiologically based pharmacokinetic (PBPK) modeling serves as a valuable tool for determining the distribution and disposition of substances in the body of an organism. It involves a mathematical representation of the interrelationships among crucial physiological, biochemical, and physicochemical parameters. A lack of the values of pharmacokinetic parameters can be challenging in constructing a PBPK model. Herein, we propose an artificial intelligence framework to evaluate a key pharmacokinetic parameter, the intestinal effective permeability (Peff). The publicly available Peff dataset was utilized to develop regression machine learning models. The XGBoost model demonstrates the best test accuracy of R-squared (R2, coefficient of determination) of 0.68. The model is then applied to compute the Peff of asiaticoside and madecassoside, the parent compounds found in Centella asiatica. Subsequently, PBPK modeling was conducted to evaluate the biodistribution of the herbal substances following oral administration in a rat model. The simulation results were evaluated and validated, which agreed with the existing in vivo studies in rats. This in silico pipeline presents a potential approach for investigating the pharmacokinetic parameters and profiles of drugs or herbal substances, which can be used independently or integrated into other modeling systems.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.1,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141302168","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

Saddlepoint p-values for a class of location-scale tests. 一类地点尺度检验的鞍点 p 值。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-10 DOI: 10.1080/10543406.2024.2358810

Abd El-Raheem M Abd El-Raheem, Haidy N Mohamed, Ehab F Abd-Elfattah

引用次数: 0