Journal of Biopharmaceutical Statistics最新文献_第5页

Direct estimation of volume under the ROC surface with verification bias. 带验证偏差的 ROC 表面下体积直接估算。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-20 DOI: 10.1080/10543406.2023.2236202

Shuangfei Shi, Gengsheng Qin

{"title":"Direct estimation of volume under the ROC surface with verification bias.","authors":"Shuangfei Shi, Gengsheng Qin","doi":"10.1080/10543406.2023.2236202","DOIUrl":"10.1080/10543406.2023.2236202","url":null,"abstract":"In practice, the receiver operating characteristic (ROC) curve of a diagnostic test is widely used to show the performance of the test for discriminating two-class events. The area under the ROC curve (AUC) is proposed as an index for the assessment of the diagnostic accuracy of the test under consideration. Due to ethical and cost considerations associated with application of gold standard (GS) tests, only a subset of the patients initially tested have verified disease status. Statistical evaluation of the test performance based only on test results from subjects with verified disease status are typically biased. Various AUC estimation methods for tests with verification biased data have been developed over the last few decades. In this article, we develop new direct estimation methods for the volume under the ROC surface (VUS) by extending the AUC estimation methods for two-class diagnostic tests to three-class diagnostic tests in the presence of verification bias. The proposed methods will provide a comprehensive guide to deal with the verification bias in three-class diagnostic test accuracy studies and lead to a better choice of diagnostic tests.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"553-581"},"PeriodicalIF":1.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10214604","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

Stochastic curtailment tests for phase II trial with time-to-event outcome using the concept of relative time in the case of non-proportional hazards. 在非比例危害的情况下，使用相对时间概念对具有时间到事件结果的 II 期试验进行随机缩减试验。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-08-14 DOI: 10.1080/10543406.2023.2244056

Palash Sharma, Milind A Phadnis

{"title":"Stochastic curtailment tests for phase II trial with time-to-event outcome using the concept of relative time in the case of non-proportional hazards.","authors":"Palash Sharma, Milind A Phadnis","doi":"10.1080/10543406.2023.2244056","DOIUrl":"10.1080/10543406.2023.2244056","url":null,"abstract":"As part of the drug development process, interim analysis is frequently used to design efficient phase II clinical trials. A stochastic curtailment framework is often deployed wherein a decision to continue or curtail the trial is taken at each interim look based on the likelihood of observing a positive or negative treatment effect if the trial were to continue to its anticipated end. Thus, curtailment can take place due to evidence of early efficacy or futility. Traditionally, in the case of time-to-event endpoints, interim monitoring is conducted in a two-arm clinical trial using the log-rank test, often with the assumption of proportional hazards. However, when this is violated, the log-rank test may not be appropriate, resulting in loss of power and subsequently inaccurate sample sizes. In this paper, we propose stochastic curtailment methods for two-arm phase II trial with the flexibility to allow non-proportional hazards. The proposed methods are built utilizing the concept of relative time assuming that the survival times in the two treatment arms follow two different Weibull distributions. Three methods - conditional power, predictive power and Bayesian predictive probability - are discussed along with corresponding sample size calculations. The monitoring strategy is discussed with a real-life example.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"596-611"},"PeriodicalIF":1.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10312957","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

Group sequential multi-arm multi-stage survival trial design with treatment selection. 分组顺序多臂多阶段生存试验设计与治疗选择。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-16 DOI: 10.1080/10543406.2023.2235409

Jianrong Wu, Yimei Li

{"title":"Group sequential multi-arm multi-stage survival trial design with treatment selection.","authors":"Jianrong Wu, Yimei Li","doi":"10.1080/10543406.2023.2235409","DOIUrl":"10.1080/10543406.2023.2235409","url":null,"abstract":"Multi-arm trials are increasingly of interest because for many diseases; there are multiple experimental treatments available for testing efficacy. Several novel multi-arm multi-stage (MAMS) clinical trial designs have been proposed. However, a major hurdle to adopting the group sequential MAMS routinely is the computational effort of obtaining stopping boundaries. For example, the method of Jaki and Magirr for time-to-event endpoint, implemented in R package MAMS, requires complicated computational efforts to obtain stopping boundaries. In this study, we develop a group sequential MAMS survival trial design based on the sequential conditional probability ratio test. The proposed method is an improvement of the Jaki and Magirr's method in the following three directions. First, the proposed method provides explicit solutions for both futility and efficacy boundaries to an arbitrary number of stages and arms. Thus, it avoids complicated computational efforts for the trial design. Second, the proposed method provides an accurate number of events for the fixed sample and group sequential designs. Third, the proposed method uses a new procedure for interim analysis which preserves the study power.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"453-468"},"PeriodicalIF":1.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9783887","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 new type of generalized information criterion for regularization parameter selection in penalized regression with application to treatment process data. 用于惩罚性回归正则化参数选择的新型广义信息准则，并将其应用于治疗过程数据。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-17 DOI: 10.1080/10543406.2023.2228399

Amir Hossein Ghatari, Mina Aminghafari

{"title":"A new type of generalized information criterion for regularization parameter selection in penalized regression with application to treatment process data.","authors":"Amir Hossein Ghatari, Mina Aminghafari","doi":"10.1080/10543406.2023.2228399","DOIUrl":"10.1080/10543406.2023.2228399","url":null,"abstract":"We propose a new approach to select the regularization parameter using a new version of the generalized information criterion (<math><mi>GIC</mi></math>) in the subject of penalized regression. We prove the identifiability of bridge regression model as a prerequisite of statistical modeling. Then, we propose asymptotically efficient generalized information criterion (<math><mi>AGIC</mi></math>) and prove that it has asymptotic loss efficiency. Also, we verified the better performance of <math><mi>AGIC</mi></math> in comparison to the older versions of <math><mi>GIC</mi></math>. Furthermore, we propose <math><mi>MSE</mi></math> search paths to order the selected features by lasso regression based on numerical studies. The <math><mi>MSE</mi></math> search paths provide a way to cover the lack of feature ordering in lasso regression model. The performance of <math><mi>AGIC</mi></math> with other types of <math><mi>GIC</mi></math> is compared using <math><mi>MSE</mi></math> and model utility in simulation study. We exert <math><mi>AGIC</mi></math> and other criteria to analyze breast and prostate cancer and Parkinson disease datasets. The results confirm the superiority of <math><mi>AGIC</mi></math> in almost all situations.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"488-512"},"PeriodicalIF":1.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9783898","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

Sample size reestimation and Bayesian predictive probability for single-arm clinical trials with a time-to-event endpoint using Weibull distribution with unknown shape parameter. 使用具有未知形状参数的 Weibull 分布，对具有时间到事件终点的单臂临床试验进行样本量再估计和贝叶斯预测概率。

IF 1.1 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-08-06 DOI: 10.1080/10543406.2023.2234998

Muhammad Waleed, Jianghua He, Milind A Phadnis

{"title":"Sample size reestimation and Bayesian predictive probability for single-arm clinical trials with a time-to-event endpoint using Weibull distribution with unknown shape parameter.","authors":"Muhammad Waleed, Jianghua He, Milind A Phadnis","doi":"10.1080/10543406.2023.2234998","DOIUrl":"10.1080/10543406.2023.2234998","url":null,"abstract":"This manuscript consists of two topics. Firstly, we explore the utility of internal pilot study (IPS) approach for reestimating sample size at an interim stage when a reliable estimate of the nuisance shape parameter of the Weibull distribution for modeling survival data is unavailable during the planning phase of a study. Although IPS approach can help rescue the study power, it is noted that the adjusted sample size can be as much as twice the initially planned sample size, which may put substantial practical constraints to continue the study. Secondly, we discuss Bayesian predictive probability for conducting interim analyses to obtain preliminary evidence of efficacy or futility of an experimental treatment warranting early termination of a clinical trial. In the context of single-arm clinical trials with time-to-event endpoints following Weibull distribution, we present the calculation of the Bayesian predictive probability when the shape parameter of the Weibull distribution is unknown. Based on the data accumulated at the interim, we propose two approaches which rely on the posterior mode or the entire posterior distribution of the shape parameter. To account for uncertainty in the shape parameter, it is recommended to incorporate its entire posterior distribution in our calculation.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"469-487"},"PeriodicalIF":1.1,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9949124","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

Using Bayesian hierarchical models for controlled post hoc subgroup analysis of clinical trials: application to smoking cessation treatment in American Indians and Alaska Natives. 使用贝叶斯分层模型对临床试验进行控制性事后亚组分析：应用于美国印第安人和阿拉斯加原住民的戒烟治疗。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-07-03 Epub Date: 2023-07-07 DOI: 10.1080/10543406.2023.2233598

Elena Shergina, Kimber P Richter, Christine Makosky Daley, Babalola Faseru, Won S Choi, Byron J Gajewski

{"title":"Using Bayesian hierarchical models for controlled post hoc subgroup analysis of clinical trials: application to smoking cessation treatment in American Indians and Alaska Natives.","authors":"Elena Shergina, Kimber P Richter, Christine Makosky Daley, Babalola Faseru, Won S Choi, Byron J Gajewski","doi":"10.1080/10543406.2023.2233598","DOIUrl":"10.1080/10543406.2023.2233598","url":null,"abstract":"Clinical trials powered to detect subgroup effects provide the most reliable data on heterogeneity of treatment effect among different subpopulations. However, pre-specified subgroup analysis is not always practical and post hoc analysis results should be examined cautiously. Bayesian hierarchical modelling provides grounds for defining a controlled post hoc analysis plan that is developed after seeing outcome data for the population but before unblinding the outcome by subgroup. Using simulation based on the results from a tobacco cessation clinical trial conducted among the general population, we defined an analysis plan to assess treatment effect among American Indians and Alaska Natives (AI/AN) enrolled in the study. Patients were randomized into two arms using Bayesian adaptive design. For the opt-in arm, clinicians offered a cessation treatment plan after verifying that a patient was ready to quit. For the opt-out arm, clinicians provided all participants with free cessation medications and referred them to a Quitline. The study was powered to test a hypothesis of significantly higher quit rates for the opt-out arm at one-month post randomization. Overall, one-month abstinence rates were 15.9% and 21.5% (opt-in and opt-out arm, respectively). For AI/AN, one-month abstinence rates were 10.2% and 22.0% (opt-in and opt-out arm, respectively). The posterior probability that the abstinence rate in the treatment arm is higher is 0.96, indicating that AI/AN demonstrate response to treatment at almost the same probability as the whole population.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"513-525"},"PeriodicalIF":1.2,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10771533/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10201603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Re: Transporting survival of an HIV clinical trial to the external target populations. 关于将 HIV 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 : 2024-06-29 DOI: 10.1080/10543406.2024.2373437

Hineptch Daungsupawong, Viroj Wiwanitkit

引用次数: 0

The generalized order statistics arising from three populations with the lower truncated proportional hazard rate models and its application to the sensitivity to the early disease stage. 下截断比例危险率模型在三个人群中产生的广义阶次统计及其在早期疾病阶段敏感性中的应用。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2024-06-22 DOI: 10.1080/10543406.2024.2365978

Hossein Nadeb, Hamzeh Torabi, Yichuan Zhao

引用次数: 0

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