Journal of Biopharmaceutical Statistics最新文献

Weighted sum and order statistics methods for dynamic information borrowing in basket trials. 篮子试验中动态信息借用的加权和序统计方法。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-08-01 DOI: 10.1080/10543406.2025.2537088

Cheng Huang, Chenghao Chu, Yimeng Lu, Bingming Yi, Ming-Hui Chen

{"title":"Weighted sum and order statistics methods for dynamic information borrowing in basket trials.","authors":"Cheng Huang, Chenghao Chu, Yimeng Lu, Bingming Yi, Ming-Hui Chen","doi":"10.1080/10543406.2025.2537088","DOIUrl":"https://doi.org/10.1080/10543406.2025.2537088","url":null,"abstract":"In basket trials, the same investigational therapy is studied on multiple sub-populations simultaneously under a single protocol. The goal of basket trials is to identify the sub-populations in which the therapy is effective. Basket trials have become a popular and generally accepted study design in disease areas including but not limited to oncology and rare diseases, for their advantages in operation and ethical considerations. Extensive research work on information borrowing has been conducted to explore the statistical efficiency in basket trials. In this paper, two novel frequentist methods for basket trials are proposed. The first method borrows information to minimize the mean squared errors in the treatment effect estimation. The second method uses information across all baskets to optimize the multiple testing task in detecting the treatment effects in each basket. Extensive simulation studies show that the proposed methods substantially improved statistical efficiency in basket trials while limiting family-wise error rate inflation. Both methods can be implemented with common statistical models with or without adjustment for covariates.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144762352","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

Defining optimal cut-off points for multiple class ROC analysis: generalization of the Index of Union method. 定义多类别ROC分析的最佳截断点：联合指数法的推广。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-07-10 DOI: 10.1080/10543406.2025.2528639

İlker Ünal, Esin Ünal, Yaşar Sertdemir, Murat Kobaner

引用次数: 0

Correction. 修正。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-07-09 DOI: 10.1080/10543406.2025.2529762

引用次数: 0

Bayesian dynamic power prior borrowing for augmenting a control arm for survival analysis. 贝叶斯动态功率先验借用，用于增强控制臂的生存分析。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-06-26 DOI: 10.1080/10543406.2025.2519153

Jixian Wang, Sanhita Sengupta, Ram Tiwari

{"title":"Bayesian dynamic power prior borrowing for augmenting a control arm for survival analysis.","authors":"Jixian Wang, Sanhita Sengupta, Ram Tiwari","doi":"10.1080/10543406.2025.2519153","DOIUrl":"https://doi.org/10.1080/10543406.2025.2519153","url":null,"abstract":"The use of real-world data, containing data from historical clinical studies, to construct an external control arm or to augment a small internal control arm in a randomized control trial can lead to significant improvements in the efficiency of the trial, but it may also introduce bias. To mitigate the risk of potential bias arising from the heterogeneity between the external control and the internal control arms, Bayesian dynamic borrowing, which determines the amount of borrowing by similarity between the two data sources, using power prior approaches and covariate adjustment has been introduced. For binary and continuous outcomes, an approach integrating propensity score for covariate adjustment and Bayesian dynamic borrowing using power prior has been proposed. Here, we extend this approach to survival analysis with the hazard ratio as the estimand. We propose a novel approach for estimating the amount of borrowing using the empirical Bayes method based on the log-hazard ratio between external and internal controls. For inference, the approach uses Bayesian bootstrap in combination with the empirical Bayes method, covariate adjustment, and multiple imputation, taking into account all uncertainty. The performance of our approach is examined by a simulation study. As an illustration, we apply the approach to dynamic borrowing of Flatiron real-world data for CheckMate-057 study for advanced non-squamous non-small cell lung cancer. For this application, we apply multiple imputation for missing covariates and propose a computationally efficient algorithm for computing the total variance of the log hazard ratio estimate. The proposed method can be applied to other endpoints in oncology as well as to other disease areas.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-22"},"PeriodicalIF":1.2,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499399","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

Explainable AI predicting Alzheimer's disease with latent multimodal deep neural networks. 可解释的人工智能预测阿尔茨海默病与潜在的多模态深度神经网络。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-06-18 DOI: 10.1080/10543406.2025.2511194

Xi Chen, Jeffrey Thompson, Zijun Yao, Joseph C Cappelleri, Jonah Amponsah, Rishav Mukherjee, Jinxiang Hu

{"title":"Explainable AI predicting Alzheimer's disease with latent multimodal deep neural networks.","authors":"Xi Chen, Jeffrey Thompson, Zijun Yao, Joseph C Cappelleri, Jonah Amponsah, Rishav Mukherjee, Jinxiang Hu","doi":"10.1080/10543406.2025.2511194","DOIUrl":"https://doi.org/10.1080/10543406.2025.2511194","url":null,"abstract":"Purpose: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline. We proposed a novel latent multimodal deep learning framework to predict AD cognitive status using clinical, neuroimaging, and genetic data.Methods: Three hundred and twenty-two patients aged between 55 and 92 from the ADNI database were included in the study. Confirmatory Factor Analysis (CFA) was applied to derive the latent scores of AD cognitive impairments as the outcome. A multimodal deep neural network with three modalities, including clinical data, imaging data, and genetic data, was constructed. Attention layers and cross attention layers were added to improve prediction; modality importance scores were calculated for interpretation. Mean Absolute Error (MAE) and Mean Squared Error (MSE) were used to evaluate the model performance.Results: The CFA demonstrated good fit to the data. The multimodal neural network of clinical and imaging modalities with attention layers was the best predictive model, with an MAE of 0.330 and an MSE of 0.206. Clinical data contributed the most (35%) to the prediction of AD cognitive status.Conclusion: Our results demonstrated the attention multimodal model's superior performance in predicting the cognitive impairment of AD, introducing attention layers into the model enhanced the prediction performance.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318759","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

Post-test medical diagnostic accuracy measures: an innovative approach based on the area under F-scores curves. 测试后医学诊断准确性测量：一种基于f分数曲线下面积的创新方法。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-06-17 DOI: 10.1080/10543406.2025.2512989

Hani Samawi, Jing Kersey, Marwan Alsharman

引用次数: 0

Assessing predictive probability of success for future clinical trials. 评估未来临床试验成功的预测概率。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-06-16 DOI: 10.1080/10543406.2025.2510262

Archie Sachdeva, Ram Tiwari, Ming Zhou

{"title":"Assessing predictive probability of success for future clinical trials.","authors":"Archie Sachdeva, Ram Tiwari, Ming Zhou","doi":"10.1080/10543406.2025.2510262","DOIUrl":"https://doi.org/10.1080/10543406.2025.2510262","url":null,"abstract":"Data-driven decision-making is crucial in drug development, with the predictive probability of success (PoS) being a key quantitative tool. PoS estimates the likelihood of success of a future trial based on the same or surrogate endpoint(s) of interest, utilizing information from interim analyses, or completed historical studies. While it has been extensively studied and broadly applied in clinical practice, there is a growing need of a unified approach for PoS that can effectively incorporate information from surrogate endpoints and multiple historical studies. This paper investigates and assesses a unified Bayesian approach for PoS. We first review PoS based on historical data on the same endpoint and then extend it to include information from a surrogate endpoint with a closed-form solution. Additionally, we utilize a Bayesian meta-analytic approach to incorporate data from multiple historical studies. We illustrate the unified approach with examples from oncology and immunology trials and provide an R package \"PPoS\" for practical implementation. By integrating the assessment of PoS with information from surrogate endpoints and historical studies, we aim to enhance the decision-making process in drug development.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-23"},"PeriodicalIF":1.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144303656","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

Quality principles in Phase I dose escalation design. 一期剂量递增设计的质量原则。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-06-13 DOI: 10.1080/10543406.2025.2512988

Jonathan M Siegel

引用次数: 0

Estimation of treatment effects in early phase randomized clinical trials involving multiple data sources for external control. 涉及多个外部控制数据源的早期随机临床试验的治疗效果评估。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-06-13 DOI: 10.1080/10543406.2025.2512984

Heiko Götte, Marietta Kirchner, Meinhard Kieser

{"title":"Estimation of treatment effects in early phase randomized clinical trials involving multiple data sources for external control.","authors":"Heiko Götte, Marietta Kirchner, Meinhard Kieser","doi":"10.1080/10543406.2025.2512984","DOIUrl":"https://doi.org/10.1080/10543406.2025.2512984","url":null,"abstract":"Augmented randomized clinical trials are a valuable design option for early phase clinical trials. The addition of external controls could, on the one hand, increase precision in treatment effect estimates or reduce the number of required control patients for a randomized trial but may, on the other hand, introduce bias. We build on previous work on augmented trials with one external control data source in time-to-event settings and extend it to multiple control data sources. In a comprehensive simulation study, we evaluate existing and novel analysis options mainly based on Bayesian hierarchical models as well as propensity score analysis. Different sources of bias are investigated including population (observable and unobservable confounders), data collection (assessment schedule, real-world vs. clinical trial data), and time trend as well as different types of data like individual patient data (with or without baseline covariates) or summary data. Our simulation study provides recommendations in terms of choice of estimation method as well as choice of data sources. Explicit incorporation of the above-mentioned sources of bias in a simulation study is relevant as the magnitude of deviation from the ideal setting has a significant impact on all investigated estimation methods.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-19"},"PeriodicalIF":1.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144295387","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

Approximate Bayesian estimation of time to clinical benefit using Frequentist approaches: an application to an intensive blood pressure control trial. 近似贝叶斯估计时间的临床效益使用频率方法：应用于强化血压控制试验。

IF 1.2 4区医学

Journal of Biopharmaceutical Statistics Pub Date : 2025-06-10 DOI: 10.1080/10543406.2025.2512985

Fang Shao, Guoshuai Shi, Zhe Lv, Duolao Wang, Mingyan Gong, Tao Chen, Chao Li

{"title":"Approximate Bayesian estimation of time to clinical benefit using Frequentist approaches: an application to an intensive blood pressure control trial.","authors":"Fang Shao, Guoshuai Shi, Zhe Lv, Duolao Wang, Mingyan Gong, Tao Chen, Chao Li","doi":"10.1080/10543406.2025.2512985","DOIUrl":"https://doi.org/10.1080/10543406.2025.2512985","url":null,"abstract":"Background: Time to Benefit (TTB) is a critical metric in clinical practice, reflecting the duration required to achieve therapeutic goals post-treatment. Traditionally, TTB estimation has relied on Bayesian Weibull regression, which, despite its merits, can be computationally intensive. To address this, we propose and evaluate Frequentist methods as efficient alternatives to approximate Bayesian TTB estimation.Methods: We evaluated three Frequentist methods, parametric delta, Monte Carlo, and nonparametric bootstrap, for TTB estimation, comparing their performance with the Bayesian approach.Results: Extensive simulations demonstrated that the proposed Frequentist methods outperformed the Bayesian method in efficiency. Real-world data applications further validated these findings, with the Monte Carlo (MC) method exhibiting significantly faster computational speed compared to the nonparametric bootstrap, while the Bayesian method was the least efficient.Conclusions: The proposed Frequentist methods offer significant advantages to approximate the Bayesian approach for TTB estimation, particularly in efficiency and practicality. The Monte Carlo method, with its median point estimate and percentile confidence intervals, is the recommended choice for its balance of efficacy and expedience.","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-11"},"PeriodicalIF":1.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259384","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