Journal of Biopharmaceutical Statistics最新文献

{"title":"Double machine learning methods for estimating average treatment effects: a comparative study.","authors":"Xiaoqing Tan, Shu Yang, Wenyu Ye, Douglas E Faries, Ilya Lipkovich, Zbigniew Kadziola","doi":"10.1080/10543406.2025.2489281","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489281","url":null,"abstract":"<p><p>Observational cohort studies are increasingly being used for comparative effectiveness research to assess the safety of therapeutics. Recently, various doubly robust methods have been proposed for average treatment effect estimation by combining the treatment model and the outcome model via different vehicles, such as matching, weighting, and regression. The key advantage of doubly robust estimators is that they require either the treatment model or the outcome model to be correctly specified to obtain a consistent estimator of average treatment effects, and therefore lead to a more accurate and often more precise inference. However, little work has been done to understand how doubly robust estimators differ due to their unique strategies of using the treatment and outcome models and how machine learning techniques can be combined to boost their performance, which we call double machine learning estimators. Here, we examine multiple popular doubly robust methods and compare their performance using different treatment and outcome modeling via extensive simulations and a real-world application. We found that incorporating machine learning with doubly robust estimators such as the targeted maximum likelihood estimator gives the best overall performance. Practical guidance on how to apply doubly robust estimators is provided.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-20"},"PeriodicalIF":1.2,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144043845","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":"Joint modeling of longitudinal endpoints and its applications to trial planning, monitoring and analysis.","authors":"Liangcai Zhang, George Capuano, Vladimir Dragalin, John Jezorwski, Kim Hung Lo, Fei Chen","doi":"10.1080/10543406.2025.2489280","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489280","url":null,"abstract":"<p><p>In the context of clinical trial practices, the study power and sample size are typically determined based on the expected treatment effects on the primary endpoint collected over time. The utilization of longitudinal modeling for the primary endpoint offers a flexible approach that has the potential to reduce the sample size and duration of the trial, thereby improving operational efficiency and costs. Joint modeling of multiple endpoints presents a unique opportunity to understand how the primary endpoint evolves over time with other clinically important endpoints, and has the potential to increase precision of estimates and therefore increase study power when designing a study at planning stage and enhance understanding and interpretation of the data at a multi-dimensional level at the analysis stage. This approach enables a comprehensive evaluation of clinical evidence from various perspectives, rather than relying solely on isolated pieces of information. Joint modeling of multiple longitudinal endpoints would also help trial monitoring process as the trial accumulates clinical evidence of efficacy data, and there is a high demand in developing tools for statistical learning the treatment benefits on the go especially when the endpoint(s) is not well-established yet in some therapeutic indications. In this article, we will illustrate the use of joint modeling of longitudinal endpoints and its applications to study design, analysis, and trial monitoring practices. Simulation studies suggest that the potential efficiency gain would be achieved via leveraging information within endpoint over time and/or between endpoints. We developed an R shiny application to aid in and support identifying promising efficacy signals from endpoints under investigation during the trial monitoring. The implementation of the joint models and the added values will be discussed through case studies and/or simulation studies.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-15"},"PeriodicalIF":1.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144041925","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":"Biomarker-guided adaptive enrichment design with threshold detection for clinical trials with time-to-event outcome.","authors":"Kaiyuan Hua, Hwanhee Hong, Xiaofei Wang","doi":"10.1080/10543406.2025.2489291","DOIUrl":"https://doi.org/10.1080/10543406.2025.2489291","url":null,"abstract":"<p><p>Biomarker-guided designs are increasingly used to evaluate personalized treatments based on patients' biomarker status in Phase II and III clinical trials. With adaptive enrichment, these designs can improve the efficiency of evaluating the treatment effect in biomarker-positive patients by increasing their proportion in the randomized trial. While time-to-event outcomes are often used as the primary endpoint to measure treatment effects for a new therapy in severe diseases like cancer and cardiovascular diseases, there is limited research on biomarker-guided adaptive enrichment trials in this context. Such trials almost always adopt hazard ratio methods for statistical measurement of treatment effects. In contrast, restricted mean survival time (RMST) has gained popularity for analyzing time-to-event outcomes because it offers more straightforward interpretations of treatment effects and does not require the proportional hazard assumption. This paper proposes a two-stage biomarker-guided adaptive RMST design with threshold detection and patient enrichment. We develop sophisticated methods for identifying the optimal biomarker threshold and biomarker-positive subgroup, treatment effect estimators, and approaches for type I error rate, power analysis, and sample size calculation. We present a numerical example of re-designing an oncology trial. An extensive simulation study is conducted to evaluate the performance of the proposed design.</p>","PeriodicalId":54870,"journal":{"name":"Journal of Biopharmaceutical Statistics","volume":" ","pages":"1-18"},"PeriodicalIF":1.2,"publicationDate":"2025-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144058373","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":"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}