Journal of Biopharmaceutical Statistics最新文献

{"title":"Study duration prediction for clinical trials with time-to-event endpoints accounting for heterogeneous population.","authors":"Hong Zhang, Jie Pu, Shibing Deng, Satrajit Roychoudhury, Haitao Chu, Douglas Robinson","doi":"10.1080/10543406.2025.2489294","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489294","url":null,"abstract":"<p><p>In the era of precision medicine, more and more clinical trials are now driven or guided by biomarkers, which are patient characteristics objectively measured and evaluated as indicators of normal biological processes, pathogenic processes, or pharmacologic responses to therapeutic interventions. With the overarching objective to optimize and personalize disease management, biomarker-guided clinical trials increase the efficiency by appropriately utilizing prognostic or predictive biomarkers in the design. However, the efficiency gain is often not quantitatively compared to the traditional all-comers design, in which a faster enrollment rate is expected (e.g. due to no restriction to biomarker positive patients) potentially leading to a shorter duration. To accurately predict biomarker-guided trial duration, we propose a general framework using mixture distributions accounting for heterogeneous population. Extensive simulations are performed to evaluate the impact of heterogeneous population and the dynamics of biomarker characteristics and disease on the study duration. Several influential parameters including median survival time, enrollment rate, biomarker prevalence and effect size are identified. Re-assessments of two publicly available trials are conducted to empirically validate the prediction accuracy and to demonstrate the practical utility.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144042977","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":"Novel machine learning approach to differential cell flow cytometry analysis based on projection pursuit.","authors":"Mahan Dastgiri, Javier Cabrera, Yajie Duan, Davit Sargsyan, Craig W Gambogi, Abraham Adokwei, Rebecca Mary Peter, PoChung Chou, Ge Cheng, Chun-Pang Lin, Jocelyn Sendecki, Helena Geys, Kanaka Tatikola, Ah-Ng Kong","doi":"10.1080/10543406.2025.2490725","DOIUrl":"https://doi.org/10.1080/10543406.2025.2490725","url":null,"abstract":"<p><p>This paper introduces the novel methodology of differential projection pursuit and its applications to the analysis of large datasets. The method was applied to a cell flow cytometry dataset as an alternative approach to analyze this type of data. Multicolor cell flow cytometry is a well-established laboratory technique to identify cell subpopulations by measuring their physical and biochemical characteristics. Differential projection pursuit helps to find regions with maximal differences between two or more treatments or distributions. Data analysis in flow cytometry relies on gating, the process of manually selecting successive subpopulations of cells using two-dimensional plots. Plotting the variables only two at a time could mask the hidden structure present in the data, and manual selection makes the analysis inconsistent and arbitrary. The new methodology could automate flow cytometry analysis by utilizing the combination of projection pursuit, data nuggets, and factor analysis. When applied to flow cytometry data, differential projection pursuit allows researchers to quickly identify differences in cell populations exposed to different experimental conditions. This methodology could create a platform to explore differences in large datasets and improve the cell flow cytometry analysis clarity and reproducibility by considering the data in its true dimensional space and through automation, respectively.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-13"},"PeriodicalIF":1.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060868","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":"A Bayesian approach for studying COVID-19 contagion dynamics in Algeria using a Poisson autoregressive (PAR) model.","authors":"Ahmed Hamimes, Hani Amir Aouissi, Feriel Kheira Kebaili, Zeinab A Kasemy","doi":"10.1080/10543406.2025.2489361","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489361","url":null,"abstract":"<p><p>Global emphasis has been focused on tracking the trends of the COVID-19 pandemic. Numerous techniques have been developed or utilized for this purpose. In this study, we seek to present and evaluate a model that, in our opinion, has not received adequate attention, using Algeria as a case study. We developed two distinct Poisson autoregressive (PAR) models using the Monte Carlo Markov Chain (MCMC) simulation method and the Bayesian method: one based solely on short-term dependence and the other incorporating both short- and long-term dependence. The study aimed to apply these models to enhance the prediction of new infections and determine whether the disease is spreading or declining. This information can guide decisions on implementing or relaxing containment measures. Our findings suggest that Algeria's epidemiological state was relatively stable at the end of the study period, with the combined long-term and short-term dependence factors being less than 1 (<math><mi>α</mi><mo>+</mo><mi>β</mi><mo>=</mo><mn>0.994</mn><mo>)</mo></math>. This indicates that while the epidemic is in decline, the infection rates are not expected to drop significantly in the near future. Furthermore, the short-term dependence parameter <math><mi>α</mi><mo>=</mo><mn>0.987</mn></math>constitutes a significant portion (99%) of the total dependence. This high value of <math><mi>α</mi></math> suggest that the COVID-19 epidemic in Algeria is experiencing a strong decline, though the rate of new infections is expected to persist at a lower level for the foreseeable future. Given these findings, it is recommended that authorities remain vigilant and continue public health measures, including educational campaigns and awareness efforts, to promote COVID-19 vaccination and adherence to health guidelines.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-17"},"PeriodicalIF":1.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144033680","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":"Identifying the clinical relative importance of each correlated outcome variables in multivariate approach: an exploration using ACCORD trial data.","authors":"Akash Mishra, N Sreekumaran Nair, K T Harichandrakumar, Binu Vs, Santhosh Satheesh","doi":"10.1080/10543406.2025.2489360","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489360","url":null,"abstract":"<p><p>In scenarios involving correlated endpoints, multivariate methods offer increased robustness for comparisons. However, understanding the individual contribution of each variable toward multivariate hypothesis rejection remains underexplored. Usually, this question is sidelined, and separate univariate analyses are performed. This paper addresses this gap by demonstrating the relative importance and contribution of variables toward the rejection of multivariate hypotheses, comparing it against a univariate approach using clinical trial data. Using the ACCORD lipid trial dataset, which includes lipid measurements of triglycerides (TG), low-density lipoprotein (LDL), and high-density lipoprotein (HDL), we employed Hotelling's T² multivariate statistic for two-group comparisons. We showcased the significance and relative importance of contributions through standardized discriminant function coefficients and partial F tests. Additionally, we investigated the impact of varying correlation levels on the significance of each variable's contribution in multivariate versus univariate approaches. Our results revealed significant lipid differences in a multivariate context at the 12th and 36th months. Across both follow-ups, TG exhibited the highest relative importance and contribution, followed by HDL and LDL. Notably, in the 36th month, the univariate approach rendered LDL's contribution insignificant for group separation, contrasting with the significant contribution identified in the multivariate approach. Furthermore, the significance likelihood of variable contributions in group separation within the multivariate approach increased with rising correlation levels. The simulation technique and the power analysis was also adopted to characterize the features of the proposed method. Our approach enables the evaluation of the relative importance and significance of each variable's contribution within the multivariate framework. This methodology holds promise for enhancing the interpretation of clinical trial analysis outcomes, particularly when dealing with multiple correlated endpoints.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-12"},"PeriodicalIF":1.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029456","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":"PS-SAM: propensity-score-integrated self-adapting mixture prior to dynamically and efficiently borrow information from historical data.","authors":"Yuansong Zhao, Peng Yang, Glen Laird, Josh Chen, Ying Yuan","doi":"10.1080/10543406.2025.2489284","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489284","url":null,"abstract":"<p><p>There has been growing interest in incorporating historical data to improve the efficiency of randomized controlled trials (RCTs) or reduce their required sample size. A key challenge is that the patient characteristics of the historical data may differ from those of the current RCT. To address this issue, a well-known approach is to employ propensity score matching or inverse probability weighting to adjust for baseline heterogeneity, enabling the incorporation of historical data into the inference of RCT. However, this approach is subject to bias when there are unmeasured confounders. We address this issue by incorporating a self-adapting mixture (SAM) prior with propensity score matching and inverse probability weighting to enable additional adaptation for information borrowing in the presence of unmeasured confounders. The resulting propensity score-integrated SAM (PS-SAM) priors are robust in the sense that if there are no unmeasured confounders, they result in an unbiased causal estimate of the treatment effect; and if there are unmeasured confounders, they provide a notably less biased treatment effect with better-controlled type I error. Simulation studies demonstrate that the PS-SAM prior exhibits desirable operating characteristics enabling adaptive information borrowing. The proposed methodology is freely available as the R package \"SAMprior\".</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-16"},"PeriodicalIF":1.2,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144027311","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":"Statistical innovation for next generation pharmaceutical development.","authors":"Zhaoyang Teng, Shibing Deng","doi":"10.1080/10543406.2025.2490327","DOIUrl":"https://doi.org/10.1080/10543406.2025.2490327","url":null,"abstract":"","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-2"},"PeriodicalIF":1.2,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144060256","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":"The role of regulatory flexibility in the review and approval process of rare disease drug development.","authors":"Shein-Chung Chow, Anne Pariser, Steven Galson","doi":"10.1080/10543406.2025.2489290","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489290","url":null,"abstract":"<p><p>The role of regulatory flexibility in the review and approval process of rare disease drug and biologics development was recently studied by a Consensus Committee of the National Academy of Sciences, Engineering and Medicine (NASEM 2024). In this article, regulatory flexibility is referred to as the exercise of scientific judgement by the regulatory agencies such as the United States Food and Drug Administration (FDA), in the review and oversight of a wide range of products, diseases and circumstances (see e.g. 21CFR Subpart E). This flexibility is intended to assist the sponsors in obtaining substantial evidence regarding safety and effectiveness of a test treatment under investigation. Applying general scientific principles, regulatory flexibility should be transparent, objective, and applied without undermining the integrity, quality and scientific validity of clinical investigation of the test treatment under study. This article attempts to provide an overview regarding the application of regulatory flexibility in rare disease drug and biologic development, which could also be applied to drug products for normal conditions. In addition, some innovative strategies and approaches which reflect regulatory flexibility and current thinking are proposed. Statistical considerations regarding the implementation of regulatory flexibility and/or current thinking in support of the demonstration of the safety and efficacy in drug development are discussed.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-12"},"PeriodicalIF":1.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144027312","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":"Use of alternative and confirmatory data in support of rare disease drug development.","authors":"Shein-Chung Chow, Anne Pariser, Steven Galson","doi":"10.1080/10543406.2025.2489279","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489279","url":null,"abstract":"<p><p>Recently, the use of alternative and confirmatory data in support of rare disease drug development has received much attention (NASEM 2024). This article attempts to provide an overview regarding the limitations and major challenges of the use of ACD that are commonly encountered in rare disease drug (including biologics) product development. In addition, some innovative approaches using ACD under a novel two-stage hybrid adaptive trial design are proposed to assist the sponsors in rare disease drug development are proposed. Under the proposed hybrid adaptive trial design, statistical considerations regarding the implementation of ACD in support of the demonstration of the safety and efficacy in rare disease drug development are discussed.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058483","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":"Quasi-Empirical Bayes methods for parameter estimation involving many small samples.","authors":"Kanaka Tatikola, Javier Cabrera, Chun Pang Lin, Helena Geys, Fetene Tekle, Jocelyn Sendecki, Stan Altan, Dhammika Amaratunga, Mariusz Lubomirski","doi":"10.1080/10543406.2025.2489357","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489357","url":null,"abstract":"<p><p>Animal studies in pharmaceutical discovery and toxicology are not always statistically powered for estimation or hypothesis testing. Typically, only 3 to 5 animals are allocated per group, based on historical conventions or industry practice, particularly in early toxicology studies with several different types of controls and compounds at various concentrations. When we estimate means, variances, or other parameters under these conditions, often the confidence intervals generated will be of little practical use due to the small sample size. If, however, historical or even concurrent data with similar characteristics is available from comparable experiments, all data could be incorporated into the estimation by using an Empirical Bayesian approach. To implement this method, the existing data is used to determine prior distributions for the parameters of interest, which are then combined with the sample data of interest to produce posterior distributions. In our case study, we combined data from 30 different experiments to use as a basis for defining the prior distributions on the mean and standard deviation (SD). For practical reasons related to our application, we prefer to use the standard deviation instead of the variance or precision that are more commonly used in the Bayesian methodology. For the mean parameter, the prior distribution is approximated by a Normal distribution, covering the range of all samples. For SD, the prior distribution is approximated with a half-Normal, half-Cauchy, or Uniform with carefully chosen boundaries. An Empirical Bayes method is then applied, combining the selected prior distributions with observed data in each small experiment to obtain the posterior distribution for the mean and for the variance of that particular experiment. The strategy of using the combined data from multiple samples to develop a common prior distribution that borrows strength across all the available data reduces the variability of the estimates and improves the estimation of individual parameters. In effect, this method combines \"borrowing strength\" with \"Empirical Bayes\" in a way that suggests \"Tukey meets Robbins\"!</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-10"},"PeriodicalIF":1.2,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144000167","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":"Reimagining optimization of clinical trials efficiency through use of statistical innovation, technology and non-standard data sources.","authors":"Kannan Natarajan, Demissie Alemayehu","doi":"10.1080/10543406.2025.2489289","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489289","url":null,"abstract":"<p><p>With the ever-growing cost of conducting traditional clinical trials and evolving regulatory paradigms, the need to deliver new medicines with speed and efficiency has become increasingly urgent. There are complex and innovative design approaches, emerging technologies, and abundant data sources that can be leveraged to address these challenges. However, their potential is not fully realized due to operational constraints and regulatory hurdles. We review the vast array of tools and technologies that are available, discuss their capabilities and limitations, and propose strategies for maximizing the efficiency of clinical trials through effective deployment of existing and new approaches.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-11"},"PeriodicalIF":1.2,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143996643","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}