Pharmaceutical Statistics最新文献

{"title":"A Dose-Finding Design for Drug Combinations Using a Bayesian 4 Parameter Logistic Model With Penalised D-Optimality.","authors":"Maxine Ajimi, Benjamin Webb, Graham M Wheeler, Jayne Marshall, Adrian P Mander","doi":"10.1002/pst.70071","DOIUrl":"10.1002/pst.70071","url":null,"abstract":"<p><p>Drug combinations, where two drugs are co-administered, are being explored widely within Phase I trials in oncology. These trials aim to identify the maximum tolerated dose combination (MTDC) by adapting the dose of both treatments based on current trial data. Various methods have been proposed to inform how escalation decisions are made within a trial. We propose a model-based design which aims to adapt the dose combination that participants receive to a level that maximises the information gained from each move across the toxicity surface, subject to admissibility and escalation constraints (penalised D-Optimality strategy). We simulated multiple different scenarios, and the penalised D-Optimality strategy is shown to have similar operating characteristics to the Interval approach, which is widely used within Phase I oncology trials. Furthermore, the penalised D-Optimality strategy shows further exploration across the toxicity surface, providing the potential to identify multiple MTDCs, further characterise the risk-benefit profile and provide flexibility for dose selection in future trials. We also show how priors of the two drugs can be specified to show a favoured escalation route if pre-clinical data suggests this.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70071"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13092417/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723495","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 Constrained Hierarchical Bayesian Model Considering Latent Biomarker Subgroups for Time-To-Event Endpoints in Randomized Phase II Trials.","authors":"Yifei Huang, Kentaro Takeda, Yongyun Zhao","doi":"10.1002/pst.70085","DOIUrl":"10.1002/pst.70085","url":null,"abstract":"<p><p>In randomized Phase III oncology trials, the long-term time-to-event endpoint is the most relevant outcome for participants and regulators. However, in Phase II trials, the short-term binary outcome of tumor response is often used as a surrogate endpoint to evaluate the treatment benefit. This may lead to a high failure rate in Phase III trials, as the tumor response may not reflect the actual survival benefit. Moreover, many oncology trials collect biomarker data, especially those that may predict clinical outcomes and identify participants who are more likely to respond to the experimental treatment. Therefore, there is a growing need for a biomarker-based design to enrich the trial by selecting participants whose biomarker levels exceed certain thresholds. This paper proposes a constrained hierarchical Bayesian model that considers latent biomarker subgroups (CHBM-LS) for long-term time-to-event endpoints in Phase II randomized trials. CHBM-LS aggregates the biomarker populations into latent subgroups and accounts for the heterogeneity of treatment effects across biomarker levels in each model. We compare our proposed design with other approaches and show the benefits of CHBM-LS in improving the accuracy of hazard ratio estimates and increasing the power to detect true effects while maintaining control over the Type I error rate.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70085"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147575124","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 Seamless Two-Stage Phase I/II Trial Design With Backfill and Joint Monitoring for Dose Optimization.","authors":"Kentaro Takeda, Jing Zhu, Belay B Yimer, Akihiro Hirakawa","doi":"10.1002/pst.70092","DOIUrl":"10.1002/pst.70092","url":null,"abstract":"<p><p>The FDA's Project Optimus initiative emphasizes dose optimization through randomized cohorts and comprehensive evaluation across dose levels. Additionally, early-phase oncology trials must efficiently evaluate antitumor activity while maintaining patient safety, requiring robust statistical frameworks for futility monitoring. We propose a seamless two-stage Phase I/II trial design integrating dose optimization with efficacy evaluation. Stage 1 employs a dose-finding approach with patient backfilling, utilizing Bayesian optimal boundaries for efficacy and toxicity to select two promising doses for further evaluation. The backfill strategy enables sequential enrollment while previous patients continue their evaluation periods, thereby accelerating the trial. Stage 2 simultaneously identifies the optimal dose and evaluates treatment effectiveness through joint monitoring of efficacy and toxicity outcomes. Stage 2 incorporates Bayesian optimal boundaries for both futility and efficacy stopping, enabling early decision-making while explicitly controlling Type I error rates. Simulation studies across realistic scenarios demonstrate superior operating characteristics of the proposed design compared to existing designs, making this approach particularly valuable for modern oncology drug development where efficiency, accuracy, and patient safety are paramount.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70092"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723555","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":"Evolving Variants, Shifting Outcomes: Implications for Nonconcurrent Controls in Platform Trials for COVID-19.","authors":"Shin Watanabe, Yosuke Shimizu, Hiroyuki Sato, Yukari Uemura, Akihiro Hirakawa","doi":"10.1002/pst.70096","DOIUrl":"10.1002/pst.70096","url":null,"abstract":"<p><p>The COVID-19 pandemic spurred the adoption of platform trials for efficient drug development. These trials evaluated multiple treatments simultaneously but faced challenges due to temporal shifts in patient characteristics, trial conduct, and standard of care. A key concern was the use of nonconcurrent control (NCC) data, which can enhance statistical power but may introduce bias if the effects of temporal shifts are not properly adjusted. In this study, we empirically evaluated temporal shifts in clinical outcomes (i.e., time to recovery through Day 28, clinical status on Day 14, and mortality through Day 28) using real-world platform trial data from the ACTIV-1 Immune Modulator trial, accounting for the evolving landscape of SARS-CoV-2 variants in the CoV-Spectrum database. We have investigated the operating characteristics of the existing methods using concurrent control and NCC data based on a resampling-based simulation study using ACTIV-1 IM trial data, as well as simulations under hypothetical variant-driven temporal-shift scenarios that explicitly modeled changes in circulating SARS-CoV-2 variants. Our empirical analyses revealed that assumptions in simulation studies-such as specific patterns of temporal shifts and constant treatment effects over time-might not hold in real-world settings. Simulation studies further showed that in such complex settings, even adjustment methods for leveraging NCC could introduce an unacceptably large bias. Our findings provided an empirical foundation for improving the integration of NCC data and ensuring reliability and efficiency. Real-world evidence underscored the need for robust statistical methods that account for the temporal shifts in platform trials.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70096"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147869304","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":"Phase III Probability of Success When Early Development Is Based on a Putative Surrogate Endpoint: A Vaccine Efficacy Case Study.","authors":"Andrea Callegaro, Francesca Solmi, Silvia Noirjean, Valentino Conti, Fabian Tibaldi, Alessia Eletti","doi":"10.1002/pst.70095","DOIUrl":"10.1002/pst.70095","url":null,"abstract":"<p><p>As Phase III trial costs and durations rise, pharmaceutical companies increasingly use quantitative methods to decide if a drug should progress beyond Phase II. A key method is the probability of success (PoS) for Phase III, calculated using the power function averaged across a treatment effect distribution estimated from Phase II. This paper explores PoS's role, particularly in moving from Phase II trials with putative surrogate endpoints to Phase III trials with clinical endpoints. Since the relationship between these endpoints is often unknown, expert input is necessary (prior elicitation). We propose the bivariate meta-analysis and a copula-based extension to characterize their relationship, using visual tools to simplify parameter elicitation. Specifically, we begin by eliciting the marginal distributions of the two quantities of interest. Then, to assist in eliciting the concordance parameter, we use the distribution of the treatment effect on the clinical endpoint conditional on the treatment effect on the putative surrogate. Our approach is illustrated in prophylactic vaccine development, linking immunological and clinical endpoints.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70095"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147819430","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":"Predictive Biomarker Graphical Approach (PRIME) for Precision Medicine.","authors":"Gina D'Angelo, Xiaowen Tian, Chuyu Deng, Xian Zhou","doi":"10.1002/pst.70094","DOIUrl":"10.1002/pst.70094","url":null,"abstract":"<p><p>Precision medicine is a dynamic and evolving field that relies on biomarkers to guide decisions about patient population enrichment, thereby shaping the direction of drug development. Determining an accurate cutoff for a biomarker is important to identify patients who are more likely to benefit from a given therapy. In conventional methods, a cutoff is derived from the minimum p-value of a dichotomized biomarker and the treatment of interest. In contrast, we have developed a predictive biomarker graphical approach, PRIME, that can be used to incorporate clinical significance and evaluate biomarkers on a continuous scale. We adapted a treatment selection approach and extended it to account for covariates via G-computation. With this proposed approach, we aim to evaluate biomarkers in a continuous manner from a predicted risk standpoint, using a model that includes the interaction between a biomarker and a treatment. The predicted risk can be graphically displayed to delineate the relationship between the outcome and the biomarker, thereby indicating a cutoff for biomarker-positive and -negative groups of patients. Other features of PRIME include the ability to compare biomarkers by using net gain summary measures and calibration to assess the model fit. The PRIME approach can be used to consider a variety of outcomes and covariates. We developed an R package, performed simulation studies, and used examples to demonstrate the PRIME approach.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70094"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147778142","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":"RABOTS: A Randomized Adaptive Bayesian Optimization Two-Stage Seamless Design for Dose Optimization and Proof-of-Concept Evaluation.","authors":"Xiaochen Zhu, Yiwei Li, Jiaju Wu, Yike Tang, Liangang Liu, Shu-Pang Huang, Hsin-Ju Hsieh","doi":"10.1002/pst.70091","DOIUrl":"10.1002/pst.70091","url":null,"abstract":"<p><p>Dose optimization (DO) is a significant paradigm in clinical trials, with the goal of identifying an optimal dose that preserves maximal efficacy while minimizing toxicity. In this paper, we propose a Randomized Adaptive Bayesian Optimization Two-stage Seamless (RABOTS) design that integrates DO with proof-of-concept (PoC) evaluation. In Stage I, an adaptive dose selection process is implemented to drop sub-optimal dose levels based on a pre-specified decision algorithm. In Stage II, a Bayesian Go/No-Go criterion is applied to the selected dose, and a dynamic borrowing approach is recommended to enhance statistical efficiency by leveraging information from the dropped dose. We evaluate the performance of RABOTS by applying different information borrowing methods across multiple dose levels through a range of simulation scenarios. Results show that the RABOTS dose selection stage reliably identifies the dose with superior efficacy and favors the lower dose when both doses exhibit similar efficacy, thereby emphasizing safety. In the PoC stage, the SAM prior borrowing method adaptively addresses prior-data conflict, effectively balancing Bayesian power and Bayesian type I error rate. We further examine how the RABOTS key tuning parameters, including the clinically meaningful effect threshold ( <math> <semantics> <mrow><msub><mi>c</mi> <mi>θ</mi></msub> </mrow> <annotation>$$ {c}_{theta } $$</annotation></semantics> </math> ) and Stage I sample size ( <math> <semantics> <mrow><msub><mi>N</mi> <mn>1</mn></msub> </mrow> <annotation>$$ {N}_1 $$</annotation></semantics> </math> ), influence its operating characteristics. The RABOTS offers a flexible and efficient framework for dose optimization and early efficacy assessment in oncology drug development. Future extensions may include more complex dose structures, the incorporation of safety and alternative endpoint types.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 3","pages":"e70091"},"PeriodicalIF":1.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147723504","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 Tobit Partly Linear Mixed and Mixture Cure Model for the Joint Analysis of Interval-Bounded Longitudinal Measurements and Survival Times With Cure Proportion.","authors":"Zhanfeng Wang, Zerui Shen, Ruixing Ming, Dongsheng Tu","doi":"10.1002/pst.70072","DOIUrl":"10.1002/pst.70072","url":null,"abstract":"<p><p>Motivated by the analysis of data from a clinical trial on patients with early breast cancer, we propose in this paper a new joint model that uses a Tobit partly linear mixed model for longitudinal measurements which are bounded in an interval and have a nonlinear relationship with the observation times and a semiparametric mixture cure model that incorporates a B-spline baseline hazard for survival times with cure proportion. A procedure is developed for estimating parameters in the proposed model using the partial likelihood and Laplace approximation. Additionally, a method of random weighting is proposed to compute the variances of the parameter estimators. The performance of the proposed model and the inference procedures is evaluated through simulation studies and data from the clinical trial that motivated this study.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70072"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146093554","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":"Leveraging Two-Stage δ Global Sensibility Analysis Method to Inform Parameter Estimation in PBPK Models.","authors":"Marina Cuquerella-Gilabert, Alessandro De Carlo, Sergio Sánchez Herrero, Javier Reig-López, Matilde Merino-Sanjuan, Elena M Tosca, Víctor Mangas-Sanjuan, Paolo Magni","doi":"10.1002/pst.70082","DOIUrl":"10.1002/pst.70082","url":null,"abstract":"<p><p>Appropriately performing Global Sensitivity Analysis (GSA) and refining models through the estimation of key parameters on individual data are fundamental steps in PBPK modeling, yet they remain insufficiently addressed in current practice. The main aim of this work is to establish a computational framework linking PhysPK with Python, enabling the application of the Two-stage δ GSA and individual parameter estimation via the iterative two-stage (ITS) method to the semi-mechanistic PBPK model Phys-DAT. The Two-Stage δ GSA was implemented to assess the impact of parameter uncertainty and correlations on key pharmacokinetic (PK) endpoints, AUC, C_max, and T_max. The most influential parameters were identified for the subsequent individual estimation by using the ITS method. Six simulated scenarios were designed by combining different sampling schedules (rich vs. sparse) and virtual sub-populations (real-case, best-case, worst-case), each reflecting specific variability patterns. Three optimization algorithms (Nelder-Mead, Powell, BFGS) were compared. Estimation performance was evaluated using Average Fold Error (AFE), Absolute AFE (AAFE), and Percentage Estimation Error (PEE). The Two-Stage δ GSA successfully identified the volume of distribution, clearance, and gastric emptying rate constant as the most influential parameters. Overall, estimation performance of the individual PK parameters and PK endpoints was provided. Most estimations yielded AFE and AAFE values between 0.8 and 1.25. Nelder-Mead showed the highest accuracy and precision. Both sampling strategy and individual variability impacted estimation quality. This work demonstrates the feasibility and value of combining correlation-aware GSA with individual parameter estimation in a semi-mechanistic PBPK framework. The integration of the Two-Stage δ GSA into PhysPK represents a major extension of the platform capabilities, providing a powerful tool to guide model simplification through dimensionality reduction of parameter space and support individual parameter estimation, especially under data-constrained conditions.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70082"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12974557/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147434673","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 Statistical Concept for Conditional Marketing Authorisation Based on the Intermediate and Final Outcomes of a Single Confirmatory Randomised Clinical Trial.","authors":"Xiaofei Liu, Nele Henrike Thomas, Elina Asikanius, Caroline Pothet, Anika Großhennig, Armin Koch","doi":"10.1002/pst.70078","DOIUrl":"10.1002/pst.70078","url":null,"abstract":"<p><p>Conditional marketing authorisation (CMA) is a path to early market access of new medicines addressing an unmet medical need in the European Union (EU), and similar concepts exist in other regulatory regions. For justifying a CMA, the benefit-risk ratio has to be positive, and the applicant must be able to provide comprehensive data post-authorisation for converting the CMA to a full marketing authorisation (MA). A recent proposal is to plan a single randomised clinical trial with interim analyses and base the decision for CMA and full MA on an intermediate and a final primary endpoint. To control the study-wise type-1-error (T1E), the dual primary endpoint concept, essentially a Bonferroni-split of the study-wise T1E between the intermediate and final endpoints, has been proposed. We argue that the resulting statistical definition of formal study success is not in line with clinical assessment of the overall trial outcome. Consistent with the European Medicines Agency (EMA) guideline on multiplicity issues in clinical trials, the intermediate and final endpoints should be co-primary, and we propose that each is assessed with a standard group sequential design. We illustrate our proposal using an oncology phase III trial with complete remission (CR) as the intermediate and overall survival (OS) as the final primary endpoint. Based on the example, we demonstrate that our proposal is a valid and flexible alternative with minimal to no impact on costs in terms of sample size or timing of the interim analysis intended for applying for a CMA but it improves the interpretation of the overall trial outcome.</p>","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70078"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12911536/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146213794","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}