Statistics in Biopharmaceutical Research最新文献

{"title":"Aligning Estimands to Strengthen the Credibility of Network Meta-Analysis: Implications for Indirect Treatment Comparisons.","authors":"Xing Xing, Lifeng Lin, Jiayi Tong, Philip He, Weili He, Haitao Chu","doi":"10.1080/19466315.2026.2703540","DOIUrl":"10.1080/19466315.2026.2703540","url":null,"abstract":"<p><p>Network meta-analysis (NMA) has become a cornerstone of evidence synthesis, enabling simultaneous comparison of multiple interventions using both direct and indirect evidence. However, recent developments in the estimand framework, formalized in ICH E9 (R1), have highlighted conceptual ambiguities in how treatment effects are defined, interpreted, and synthesized in complex evidence networks. We review the core components of the estimand framework and evaluate how each is handled in standard NMA practice. Particular attention is given to heterogeneity in strategies for handing intercurrent events across trials, including treatment switching and non-adherence, and to the reliance of NMA on transitivity and consistency assumptions when underlying estimands may not be aligned. We identify several key challenges in the estimation for NMA. First, individual trials within a network may target different estimands, despite nominally similar comparisons, leading to incoherent synthesis targets. Second, NMA models may implicitly combine estimands aligned with different intercurrent event strategies, complicating causal interpretation of NMA estimates. Failure to align estimands across trials and comparisons can compromise transitivity, distort treatment rankings, and mislead decision-making. We argue that incorporating the estimand framework formally into NMA design, analysis, and reporting is critical for improving the transparency, interpretability, and credibility of evidence synthesized by NMA.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13458990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714559","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}

{"title":"An Adaptive Biomarker-based Umbrella Trial Design Using Bayesian Latent Class Model.","authors":"Jiaying Guo, Mengyi Lu, Yan Han, Leng Han, Yong Zang","doi":"10.1080/19466315.2026.2642204","DOIUrl":"10.1080/19466315.2026.2642204","url":null,"abstract":"<p><p>A Bayesian adaptive umbrella trial design is proposed to identify effective treatments for biomarker-defined subgroups. This design employs a mixture regression model to jointly model short-term binary and long-term time-to-event outcomes and introduces a latent class model to adaptively cluster subgroups with similar treatment-outcome distributions. The model aims to dynamically borrow information between subgroups in the same class, enhancing the efficiency and robustness in testing subgroup-specific treatment effects. To strengthen the individual ethics of the trial, the design incorporates response-adaptive randomization with early stopping rules for futility and superiority. The simulation results confirm that the proposed design demonstrates desired performance.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266917","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}

{"title":"A Meta-analysis based Hierarchical Variance Model for Powering One and Two-sample <i>t</i>-tests.","authors":"Jackson Barth, Ren-Cang Li, Xinlei Wang","doi":"10.1080/19466315.2026.2637572","DOIUrl":"10.1080/19466315.2026.2637572","url":null,"abstract":"<p><p>Sample size determination (SSD) is essential in statistical inference and hypothesis testing, as it directly affects the accuracy and power of the analysis. We propose a SSD methodology for one and two-sample t-tests that ensures clinical relevance using a pre-determined unstandardized effect size. Our novel approach leverages Bayesian meta-analysis to account for the uncertainty surrounding the variance, a common issue in SSD. By incorporating prior knowledge from related studies via a Bayesian gamma-inverse gamma model, we obtain an informative posterior predictive distribution for the variance that leads to better decisions about sample size. For efficient posterior sampling, we propose an empirical Bayes approach which is implemented with systematic sampling to facilitate computation. Simulations and empirical studies demonstrate that our methodology outperforms other aggregate approaches (simple average, weighted average, median) in variance estimation for SSD, especially in meta-analyses with large disparity in sample size and moderate variance. Thus, it offers a robust and practical solution for sample size determination in t-tests.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13559623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148931574","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}

{"title":"A Bayesian Adaptive Marker-Stratified Design for Phase II Clinical Trials Using Calibrated Spike-and-Slab priors.","authors":"Mu Shan, Mengyi Lu, Leng Han, Yan Han, Yong Zang","doi":"10.1080/19466315.2026.2616369","DOIUrl":"10.1080/19466315.2026.2616369","url":null,"abstract":"<p><p>The marker-stratified design (MSD) is useful for assessing subgroup-specific treatment effects of molecularly targeted agents (MTA). In MSD, patients are first classified into the marker-positive and marker-negative subgroups, and then randomized for MTA or control treatment within each subgroup. The clinical features of the biomarker and treatments used in MSD offer valuable information for treatment evaluation. Specifically, response rates for patients on the control treatment remain similar across different subgroups if the biomarker involved is not prognostic. Additionally, when the MTA is effective, the marker-positive patients generally exhibit significantly higher response rates compared to the marker-negative patients receiving the same MTA. This paper proposes a Bayesian adaptive design (SSS) that uses these clinical features to enhance the efficiency of MSD. The SSS design employs spike-and-slab priors to dynamically borrow information on response rates across different subgroups. The strength of this information borrowing is automatically determined by two posterior probabilities, which measure the similarities in response rates between different subgroups. Furthermore, an extension of the SSS design is proposed to accommodate patients with missing biomarker profiles, utilizing the Bayesian multiple imputation (BMI) method. Simulation studies confirm that the proposed SSS design demonstrates favorable operational characteristics and outperforms the conventional Bayesian designs.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13268572/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148266996","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}

{"title":"Group Sequential Design and Monitoring of Clustered Data in Randomized Eye Trials.","authors":"Guoqing Diao, Qiang Zhang, Toshimitsu Hamasaki, Scott Evans","doi":"10.1080/19466315.2025.2566339","DOIUrl":"10.1080/19466315.2025.2566339","url":null,"abstract":"<p><p>Group sequential designs are the most commonly used methods for randomized clinical trials with interim monitoring plans, which control the type I error rate and protect statistical power. However, to the best of our knowledge, group sequential methods accounting for the inter-eye correlation under a unified design in randomized eye trials, do not exist in the literature. Failing to account for inter-eye correlations may lead to unnecessary study continuation or early stopping. We propose a unified group sequential design for vision research that accommodates all possible design options while accounting for inter-eye correlations when both eyes of a patient are included in the study for at least some patients. We derive closed-form formulae for the sample size/power calculations for fixed-sample designs and the information fractions during interim monitoring for group sequential designs while accounting for the inter-eye correlation for continuous endpoints. Simulation studies and an application to the Age-Related Eye Disease Study are provided. An R package <i>iTrial</i> implementing the proposed methodology is publicly available.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13344445/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148413742","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}

{"title":"On the Two-Step Hybrid Design for Augmenting Randomized Trials Using Real-World Data.","authors":"Jiapeng Xu, Ruben P A van Eijk, Alicia Ellis, Tianyu Pan, Lorene M Nelson, Kit C B Roes, Marc van Dijk, Maria Sarno, Leonard H van den Berg, Lu Tian, Ying Lu","doi":"10.1080/19466315.2025.2547855","DOIUrl":"10.1080/19466315.2025.2547855","url":null,"abstract":"<p><p>Hybrid clinical trials, which borrow real-world data (RWD) from patient registries, claims databases, or electronic health records (EHRs) to augment randomized clinical trials, are of increasing interest. Hybrid clinical trials are especially relevant for rare diseases, where the recruitment of large sample sizes may be challenging. While these trials may better use available information, they assume that the RWD and randomized control arm are exchangeable. Violating this assumption can induce bias, inflate Type I error, or adversely affect statistical power. A two-step hybrid design first tests the exchangeability between randomized control arm and external data sources before incorporating RWD as a comparator for statistical inferences (Yuan et al. 2019). This approach reduces the chance of inappropriate borrowing but may simultaneously inflate the Type I error rate. We propose four different methods to control the Type I error rate under the exchangeability assumption. Approach 1 estimates the variance of the overall test statistic and rejects the null hypothesis based on a Z-test. Approach 2 uses a numerical method to determine the exact critical value for Type I error control. Approach 3 splits the Type I error rates according to the equivalence test outcome. Approach 4 adjusts the critical value only when equivalence is established. We illustrate these methods using a hypothetical scenario in the context of amyotrophic lateral sclerosis (ALS). We evaluate the Type I error and power under various clinical trial conditions in comparison with the Bayesian power prior approach (Ibrahim et al. 2015). We demonstrate that our proposed methods and Bayesian power prior control Type I error and increase power under the exchangeability assumption, whereas the method proposed by Yuan et al. (2019) results in an increased Type I error. In the scenario where the exchangeability assumption does not hold, all methods fail to control the Type I error. Our proposed methods, however, limit a maximum Type I error inflation ranging from 6% to 8%, which compares favorably to 10% for Yuan et al. (2019) and 16% for the Bayesian power prior. All methods increase statistical power under the exchangeability condition but may lead to a loss of statistical power when the exchangeability assumption is violated.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12539643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145349772","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}

{"title":"A novel longitudinal rank-sum test for multiple primary endpoints in clinical trials: Applications to neurodegenerative disorders.","authors":"Xiaoming Xu, Dhrubajyoti Ghosh, Sheng Luo","doi":"10.1080/19466315.2025.2458018","DOIUrl":"10.1080/19466315.2025.2458018","url":null,"abstract":"<p><p>Neurodegenerative disorders such as Alzheimer's disease (AD) present a significant global health challenge, characterized by cognitive decline, functional impairment, and other debilitating effects. Current AD clinical trials often assess multiple longitudinal primary endpoints to comprehensively evaluate treatment efficacy. Traditional methods, however, may fail to capture global treatment effects, require larger sample sizes due to multiplicity adjustments, and may not fully utilize the available longitudinal data. To address these limitations, we introduce the Longitudinal Rank Sum Test (LRST), a novel nonparametric rank-based omnibus test statistic. The LRST enables a comprehensive assessment of treatment efficacy across multiple endpoints and time points without the need for multiplicity adjustments, effectively controlling Type I error while enhancing statistical power. It offers flexibility for various data distributions encountered in AD research and maximizes the utilization of longitudinal data. Simulations across realistic clinical trial scenarios, including those with conflicting treatment effects, and real-data applications demonstrate the LRST's performance, underscoring its potential as a valuable tool in AD clinical trials.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12352412/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144978017","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}

{"title":"Type I Error Control of an Adaptive Endpoint Selection Procedure","authors":"Nan Hu, Godwin Yung, Howard Mackey","doi":"10.1080/19466315.2025.2455173","DOIUrl":"https://doi.org/10.1080/19466315.2025.2455173","url":null,"abstract":"In clinical trials, a single primary endpoint or a primary endpoint family is traditionally pre-specified before trial initiation. While in general the primary endpoint(s) of a trial should not be altered thereafter, there may be situations where the pre-specified primary analysis no longer makes sense. For example, when endpoint sensitivity is markedly less than expected. To investigate the operating characteristics of endpoint adaptation, we present two approaches to tackle this problem: primary endpoint adaptation based on (a) unblinded or (b) blinded estimate of the coefficient of variation from the control group. We show that the adaptation method based on blinded data introduced in this article has strong control of the family-wise Type I error, while the adaptation based on unblinded data does not. We examine the asymptotic conditions under which the adaptation based on blinded data correctly chooses the most powerful endpoint. We also compare the performance of the blinded adaptation with other potential approaches. We present a case study illustrating how the proposed blinded adaptation could have been used in a real clinical trial. Finally, we provide recommendations on how to implement the adaptation in practice.","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"17 4","pages":"576-589"},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147903314","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}

{"title":"Two-stage Adaptive Enrichment Designs with Survival Outcomes and Adjustment for Misclassification in Predictive Biomarkers.","authors":"Yanping Chen, Yong Lin, Shou-En Lu, Weichung Joe Shih, Hui Quan","doi":"10.1080/19466315.2024.2395408","DOIUrl":"10.1080/19466315.2024.2395408","url":null,"abstract":"<p><p>Biomarker enrichment clinical trial designs are versatile tools to assess the treatment effect and increase the efficiency of clinical trials. In this paper, we propose a two-stage enrichment clinical trial design with survival outcomes, and consider the situation where the biomarker assay and classification are possibly subject to errors. Specifically, the first stage is a randomized design, stratified by the biomarker appeared status. Depending on the result of the interim analysis and a pre-specified futility criterion, the second stage can be either enriched with only the biomarker appeared positive patients, or remain as the stratified design with both biomarker appeared positive and biomarker appeared negative patients. Compared to continuous and binary outcomes, test statistics to account for biomarker misclassification are much more complicated and require special care. We develop log-rank statistics for the interim and final analyses, with an adjustment for the sensitivity and specificity of the biomarker assay. Control of Type I error rate is achieved by considering correlations between adjusted log-rank statistics from the same and/or different stages. R code is developed to calculate critical values, global/marginal power, and sample size. Our method is illustrated with examples of a recently successful development of immunotherapy in non-small-cell lung cancer.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"17 3","pages":"425-445"},"PeriodicalIF":1.6,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12530146/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145330893","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}

{"title":"Assessment of treatment effect heterogeneity for multiregional randomized clinical trials.","authors":"Haotian Zhuang, Xiaofei Wang, Stephen L George","doi":"10.1080/19466315.2024.2421748","DOIUrl":"10.1080/19466315.2024.2421748","url":null,"abstract":"<p><p>Multiregional clinical trials (MRCTs) have become increasingly common in recent years. Detecting underlying regional heterogeneity is a critical issue for these trials. Existing methods for assessing treatment effect heterogeneity across regions have ignored the incomparability of baseline extrinsic risk factors of the randomized patients from different regions. In this paper, a calibration weighting method is proposed to calibrate the distribution of these extrinsic risk factors between multiple regions. We establish the consistency and the asymptotic normality of the calibration weighting estimator. Simulation studies confirm the finite sample properties of the proposed estimator as well as its superior performance over naive methods and the inverse probability weighting method. The proposed method is illustrated using a randomized clinical trial of adjuvant chemotherapy for resected non-small-cell lung cancer.</p>","PeriodicalId":51280,"journal":{"name":"Statistics in Biopharmaceutical Research","volume":"17 3","pages":"315-322"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12312651/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144776849","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}