Journal of Biopharmaceutical Statistics最新文献

{"title":"A demonstration of estimands and sensitivity analyses for time-to-deterioration of patient reported outcomes.","authors":"Lysbeth Floden, Michael DeRosa, Jessica Roydhouse, Jennifer L Beaumont, Stacie Hudgens","doi":"10.1080/10543406.2024.2341649","DOIUrl":"10.1080/10543406.2024.2341649","url":null,"abstract":"<p><p>In oncology trials, health-related quality of life (HRQoL), specifically patient-reported symptom burden and functional status, can support the interpretation of survival endpoints, such as progression-free survival. However, applying time-to-event endpoints to patient-reported outcomes (PRO) data is challenging. For example, in time-to-deterioration analyses clinical events such as disease progression are common in many settings and are often handled through censoring the patient at the time of occurrence; however, disease progression and HRQoL are often related leading to informative censoring. Special consideration to the definition of events and intercurrent events (ICEs) is necessary. In this work, we demonstrate time-to-deterioration of PRO estimands and sensitivity analyses to answer research questions using composite, hypothetical, and treatment policy strategies applied to a single endpoint of disease-related symptoms. Multiple imputation methods under both the missing-at-random and missing-not-at-random assumptions are used as sensitivity analyses of primary estimands. Hazard ratios ranged from 0.52 to 0.66 over all the estimands and sensitivity analyses modeling a robust treatment effect favoring the treatment in time to disease symptom deterioration or death. Differences in the estimands include how people who experience disease progression or discontinue the randomized treatment due to AEs are accounted for in the analysis. We use the estimand framework to define interpretable and principled approaches for different time-to-deterioration research questions and provide practical recommendations. Reporting the proportions of patient events and patient censoring by reason helps understand the mechanisms that drive the results, allowing for optimal interpretation.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"918-932"},"PeriodicalIF":1.2,"publicationDate":"2025-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140857008","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}

{"title":"Defining optimal cut-off points for multiple class ROC analysis: generalization of the Index of Union method.","authors":"İlker Ünal, Esin Ünal, Yaşar Sertdemir, Murat Kobaner","doi":"10.1080/10543406.2025.2528639","DOIUrl":"https://doi.org/10.1080/10543406.2025.2528639","url":null,"abstract":"<p><p>A variety of well-developed methodologies exist for the purpose of binary classification. Some of these methodologies have been extended to accommodate multi-class settings with three or even more classes. In this study, we generalize the Index of Union (IU) method, which we previously demonstrated to be more effective than other methods in binary classification. We evaluate the Generalized Index of Union (GIU) method and compare it with existing methods using both simulated and real data. The results of the comparisons demonstrated that the GIU method is an effective approach in a multitude of scenarios, including those involving high volume under the surface (VUS) values and all distributions. It is therefore recommended that the GIU method can be used to determine the optimal cut-off points in all the ROC analyses due to its structure, which does not require complex calculations and thus provides fast results.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-19"},"PeriodicalIF":1.2,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602282","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><strong>Purpose: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","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}

{"title":"Post-test medical diagnostic accuracy measures: an innovative approach based on the area under F-scores curves.","authors":"Hani Samawi, Jing Kersey, Marwan Alsharman","doi":"10.1080/10543406.2025.2512989","DOIUrl":"https://doi.org/10.1080/10543406.2025.2512989","url":null,"abstract":"<p><p>Clinicians have increasingly turned to F-scores to gauge the accuracy of diagnostic tests. However, the dependency of F-scores on the prevalence of the underlying illness poses challenges, especially when prevalence varies across regions or populations, potentially leading to misdiagnoses. To address this issue, this article presents novel post-test diagnostic precision metrics for continuous tests or biomarkers. These metrics are based on the collective areas under the F-score curves across all conceivable prevalence values. Unlike traditional measures, the proposed metrics remain constant regardless of disease prevalence, enabling fair comparisons of different diagnostic tests and biomarkers' abilities in rule-in, rule-out, and overall accuracy. The article also explores the relationship between the proposed metrics and other diagnostic accuracy measures. Numerical illustrations and a real-world breast cancer dataset exemplify the application of the proposed metrics.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144318760","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}

{"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":"<p><p>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.</p>","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}

{"title":"Quality principles in Phase I dose escalation design.","authors":"Jonathan M Siegel","doi":"10.1080/10543406.2025.2512988","DOIUrl":"https://doi.org/10.1080/10543406.2025.2512988","url":null,"abstract":"<p><p>This paper discusses quality principles for Phase I model-based dose escalation design. It emphasizes that a loss function underlying a dose escalation trial estimator can be usefully interpreted as a quantified representation of the ethical assumptions underlying the treatment decisions to be made in the trial. Based on this principle, it discusses additional general quality design principles developers of clinical trial design methods should consider, including the role of continuous loss functions in quality per Taguchi, and per Deming the role of asymmetric loss functions and the importance of understanding the underlying process and its order of operations. It provides a number of model-based dose escalation designs as examples, including the mTPI as an introductory example, the EWOC design, and the CRM and modifications to it. It introduces some foundational scientific underpinnings and principles of quality philosophy, and explains how the principles apply to the examples. It stresses the importance of an engineering process by which a study is designed to meet identified and investigated user requirements.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-9"},"PeriodicalIF":1.2,"publicationDate":"2025-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144287139","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}

{"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":"<p><p>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.</p>","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}

{"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":"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>We evaluated three Frequentist methods, parametric delta, Monte Carlo, and nonparametric bootstrap, for TTB estimation, comparing their performance with the Bayesian approach.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusions: </strong>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.</p>","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}

{"title":"Penalized Bayesian methods for product ranking using both positive and negative references.","authors":"Clement Laloux, Bruno Boulanger, Philippe Bastien, Bradley P Carlin, Arnaud Monseur, Carole Guillou, Daiane Garcia Mercurio, Hussein Jouni","doi":"10.1080/10543406.2025.2489287","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489287","url":null,"abstract":"<p><p>Product ranking according to pre-specified criteria is essential for developing new technologies, allowing identification of more preferable candidates for further development. Such ranking often builds on the results of a network meta-analysis, where the relative or absolute performances of the various products are synthesized across multiple clinical studies, each of which considered only a subset of the products. Ranking involving both a negative and a positive reference enables the scientist to directly compare tested products against known benchmarks. Here, more preferable candidates are those products that approach the positive reference while remaining distant from the negative reference. We provide a new metric to quantify this multivariate distance following Bayesian meta-analysis. Our method does not simply rely on point estimates to perform the comparisons, but also accounts for their uncertainties via their posterior distributions. For each product, posterior probabilities of being comparable to the positive reference are computed, and subsequently penalized by the posterior probability of performing worse than the negative reference. Each product is then compared to a hypothetical product about which we have no knowledge, as captured by a uniform distribution. The result is a prospective metric that is directly interpretable as the improvement of any product beyond this state of ignorance. We illustrate our approach using a case study, in which the goal is to rank 16 antiperspirant products. Here, the FDA-recommended summary statistic (a measure of the relative sweat reduction between each product and no treatment) intrinsically features both positive and negative references. We then offer a brief simulation study to check our metric's performance in less complex, idealized settings where the true ranking is known. Our results indicate that our Bayesian approach is a novel and useful addition to the statistical ranking toolkit.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144259385","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}