Pharmaceutical Statistics最新文献_第4页

Bayesian Power-Based Sample Size Determination for Single-Arm Clinical Trials With Time-to-Event Endpoints. 具有事件时间终点的单臂临床试验中基于贝叶斯功率的样本量确定。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70087

Go Horiguchi, Isao Yokota, Satoshi Teramukai

{"title":"Bayesian Power-Based Sample Size Determination for Single-Arm Clinical Trials With Time-to-Event Endpoints.","authors":"Go Horiguchi, Isao Yokota, Satoshi Teramukai","doi":"10.1002/pst.70087","DOIUrl":"10.1002/pst.70087","url":null,"abstract":"The purpose of an exploratory clinical trial is to determine whether a new treatment is worth evaluating in subsequent trials. These trials often assessed the efficacy and safety of a single-arm design with binary outcomes. In cancer therapy, time-to-event may be the primary endpoint. In such cases, the frequentist method is typically used to determine sample size. The Bayesian method has recently attracted attention from the perspective of using prior information and introducing early termination criteria. We propose a sample size determination method based on Bayesian power using the posterior probability and prior predictive probability of the hazard ratio (parameter), assuming that the survival functions of historical control and new treatment have proportional hazards. The prior information of the parameter is expressed as the analysis prior and the uncertainty of the parameter is expressed as the design prior. To conduct the clinical trial efficiently, we extended the study design to include early termination criteria. The simulation results showed that the sample size decreased when using an informative analysis prior, whereas it increased when using a design prior that accounted for variance, thus allowing for a more conservative sample size design while taking advantage of the available prior information.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70087"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468855","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}

引用次数: 0

New, Shorter Small-Sample Intervals for Vaccine Efficacy. 新的、更短的疫苗效力小样本间隔。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70077

Mauro Gasparini, Vincenzo Di Trani, Marco Ratta

{"title":"New, Shorter Small-Sample Intervals for Vaccine Efficacy.","authors":"Mauro Gasparini, Vincenzo Di Trani, Marco Ratta","doi":"10.1002/pst.70077","DOIUrl":"10.1002/pst.70077","url":null,"abstract":"In this work we illustrate a method to improve estimation of Vaccine Efficacy (VE), a vastly employed measure of effect in vaccine clinical research, with small and medium sample sizes. We introduce a comprehensive Bayesian approach that improves upon existing methodologies, explicitly considering patient recruitment processes to inform parameter estimation. In particular-in contrast to most methods currently used-in our proposed methodology the total number of cases as well as the censored surveillance times are seen as informative statistics, with underlying distributions which are used to derive the full likelihood. It turns out that our model depends on first and second moments of the surveillance times regardless of the recruitment process and, for finite sample sizes, it improves on the maximum likelihood method, which depends only on the first moment. The methodology is validated through extensive numerical simulations, demonstrating substantial improvements in parameter interval estimation across diverse scenarios and under multiple recruitment plans, when the number of events-and roughly the sample sizes-are small to medium. For large sample sizes, our method is equivalent to maximum likelihood. Markov Chain Monte Carlo (MCMC) simulations are needed and can be conducted very efficiently, due to an appropriate parameterization.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70077"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12914770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146220397","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}

引用次数: 0

Evaluating Safety Rules in Ongoing Dose-Escalation Trials Using BOIN: A Comparative Analysis of Criteria and Their Impact on Dose-Level Safety Assessments. 使用BOIN评价正在进行的剂量递增试验中的安全规则：标准的比较分析及其对剂量水平安全评估的影响。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70075

Stefan Englert, Thomas J Prior, Anirban Mitra, Busola Sanusi, Liangcai Zhang, Lilla Di Scala

{"title":"Evaluating Safety Rules in Ongoing Dose-Escalation Trials Using BOIN: A Comparative Analysis of Criteria and Their Impact on Dose-Level Safety Assessments.","authors":"Stefan Englert, Thomas J Prior, Anirban Mitra, Busola Sanusi, Liangcai Zhang, Lilla Di Scala","doi":"10.1002/pst.70075","DOIUrl":"10.1002/pst.70075","url":null,"abstract":"Ongoing dose-escalation trials present unique challenges in assessing safety, all with the goal to establish the maximum tolerated dose (MTD) and/or the recommended phase II dose (RP2D). Due to the extended duration of trials, it is common to declare doses as safe even if the entire adaptive and iterative dose escalation process has not been completed. This practice is often applied to aid trial management decisions, such as starting region-specific dose-escalation studies, initiating combination therapies, or allowing intra-patient dose escalation in previous cohorts, to enhance efficiency and accelerate the pace of drug development. Whereas regulatory agencies often require a safety margin for the starting dose for regional or combination dose-escalation studies, there exists a misperception within the research community that once an escalation assessment team has cleared a cohort for escalation, the associated dose level is inherently safe. Given that dose escalation algorithms permit repeated de-escalations, previously cleared doses may not be truly safe, necessitating a quantification of this risk and identifying an adequate safety margin. Through rigorous simulation studies, we identified criteria that must be met to confidently declare dose levels as safe within the Bayesian optimal interval (BOIN) design. Our comparative analysis of safety criteria shows that evaluating a minimum of three subjects at the dose level above the one to be declared safe is essential for accurate safety assessments and protecting patients. More aggressive dose-escalation paradigms would risk exposing patients to doses that are not ultimately declared safe.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70075"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146166176","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}

引用次数: 0

Methodological Approaches for the Estimation of Confidence Intervals on Partial Youden Index Under Verification Bias. 验证偏差下偏约登指数置信区间估计的方法学方法。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70079

Sihan Jia, Shirui Wang, Gengsheng Qin

{"title":"Methodological Approaches for the Estimation of Confidence Intervals on Partial Youden Index Under Verification Bias.","authors":"Sihan Jia, Shirui Wang, Gengsheng Qin","doi":"10.1002/pst.70079","DOIUrl":"10.1002/pst.70079","url":null,"abstract":"The advancement of precision medicine hinges on accurately tailored diagnostic strategies yet estimating reliable confidence intervals (CIs) for the maximal partial Youden Index under verification bias presents considerable challenges, especially within critical false positive rate (FPR) ranges (e.g., (0, 0.1), (0.05, 0.2)) vital for specific clinical applications. While previous work established the partial Youden Index framework, and methods like full imputation (FI), mean score imputation (MSI), inverse probability weighting (IPW), and semiparametric efficient (SPE) address verification bias, robustly integrating these for the partial index across demanding FPRs has needed further development. This paper significantly advances this area by adapting and applying these four bias-correction techniques to estimate the partial Youden Index and its confidence interval (CIs) under verification bias. We systematically evaluate their performance with the proposed (bootstrap-based, MOVER) CI construction approaches. Extensive simulations demonstrate distinct method-specific patterns across verification proportions and FPR ranges, revealing the complexities in achieving reliable estimates. Bootstrap-based CIs exhibit greater robustness to model misspecification, a common clinical uncertainty, while analytical CIs often face undercoverage issues. A cardiovascular disease biomarker analysis corroborates these findings, showing Blood Pressure's superior discriminatory capability compared to Pulse Rate. Operating under the Missing at Random (MAR) assumption, these results offer crucial, updated guidance for CI estimation in diagnostic studies with incomplete verification, providing significant value where precise evaluation in specific FPR regions is paramount and complete verification is unfeasible. Our findings enhance the statistical foundation for diagnostic test evaluation, extending beyond previous work by comprehensively addressing the partial Youden Index with these updated verification bias correction and CI formula applications.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70079"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146220432","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}

引用次数: 0

Penalized Variable Selection for Joint AFT Random-Effect Model With Clustered Competing-Risks Data. 聚类竞争风险联合AFT随机效应模型的惩罚变量选择。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70084

Lin Hao, Il Do Ha

引用次数: 0

Application of Causal Inference to Establish Assay Effect in the Absence of a Bridging Study: A Case Study of MenACWY-CRM Conjugate Vaccine Data. 在缺乏桥接研究的情况下，应用因果推理建立检测效果：以MenACWY-CRM结合疫苗数据为例

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70069

Meike Adani, Silvia Noirjean, Andrea Callegaro, Pavitra Keshavan, Marco Costantini

{"title":"Application of Causal Inference to Establish Assay Effect in the Absence of a Bridging Study: A Case Study of MenACWY-CRM Conjugate Vaccine Data.","authors":"Meike Adani, Silvia Noirjean, Andrea Callegaro, Pavitra Keshavan, Marco Costantini","doi":"10.1002/pst.70069","DOIUrl":"10.1002/pst.70069","url":null,"abstract":"During a vaccine development program, if the assay used to measure immunological endpoints is changed, ideally, a bridging study is performed to establish the relationship between results obtained with the new and previous assay. However, this is not always feasible, and when bridging study data are absent, this can limit the ability to use historical study information to strengthen evidence generated in the clinical program. We present a case study on GSK's quadrivalent meningococcal vaccine (MenACWY-CRM), where the immunogenicity assay was changed over time. A large amount of study data was collected in randomized controlled clinical trials, providing a valuable source of information to support vaccine development, but the introduction of the new assay complicated the comparison of antibody responses across studies. Several causal inference techniques, developed for the analysis of non-randomized studies, can be used to estimate the assay bridging effect and, as observed in our case study, address the presence of confounding factors resulting from pooling group data from different sources. Cutting-edge propensity score-based methods were evaluated, highlighting their advantages and limitations. Within the family of propensity score weighting methods, the widely used inverse probability weighting was compared to the novel overlap weighting technique. The latter was shown to resolve the problem of extreme weights in a situation where there was poor overlap in covariate distribution between two groups. Automated selection of specific methods should be approached with caution, carefully considering the different estimands targeted by different methods.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70069"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12857608/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146086732","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}

引用次数: 0

An Association Test for Ordinal Outcomes in Clustered Data With Informative Cluster Size. 具有信息聚类大小的聚类数据中有序结果的关联检验。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70089

Hasika K Wickrama Senevirathne, Sandipan Dutta

{"title":"An Association Test for Ordinal Outcomes in Clustered Data With Informative Cluster Size.","authors":"Hasika K Wickrama Senevirathne, Sandipan Dutta","doi":"10.1002/pst.70089","DOIUrl":"10.1002/pst.70089","url":null,"abstract":"In cluster-correlated data, the number of observations in a cluster can be associated with the outcome from that cluster. This phenomenon is known as informative cluster size which can occur in cluster-randomized clinical trial data. Several studies have found that ignoring the issue of informative cluster size can produce biased results in the analysis of clustered data. Most of the existing methods for addressing informative cluster size are suited to continuous outcomes. However, ordinal outcomes and covariates are often encountered in clustered data obtained from large clinical studies. The existing methods for ordinal association testing in clustered data can produce suboptimal results in the presence of informative cluster size. In this article, we propose a new nonparametric method for testing marginal association between ordinal variables in clustered data that can account for informative cluster size. Through simulated data analyses, we show that our new test outperforms the existing alternatives in accurately identifying significant marginal ordinal associations in the presence of informative cluster size. Even if the cluster size is not informative, the performance of our method is comparable to the existing methods. Additionally, we demonstrate the usefulness of our proposed method through an application to a real-world cluster-randomized clinical trial data.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70089"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12990042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468840","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}

引用次数: 0

Paired and AB/BA Cross-Over Design in Early Phase Clinical Trials: A Closer Look at Within-Subject Variance Bias. 早期临床试验中的配对和AB/BA交叉设计：更仔细地观察受试者内方差偏倚。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70088

Martin J Wolfsegger, Peixin Xu, Amy Cotterill, Helmut Schütz, Thomas F Jaki

{"title":"Paired and AB/BA Cross-Over Design in Early Phase Clinical Trials: A Closer Look at Within-Subject Variance Bias.","authors":"Martin J Wolfsegger, Peixin Xu, Amy Cotterill, Helmut Schütz, Thomas F Jaki","doi":"10.1002/pst.70088","DOIUrl":"10.1002/pst.70088","url":null,"abstract":"This manuscript advocates for the implementation of multiple-sequence cross-over designs in early-phase clinical trials by investigating the bias in within-subject variance present in paired and AB/BA cross-over clinical trial designs. While the advantages of adding additional sequences to mitigate confounding effects are well established, the authors noted a lack of mathematical discussion regarding the estimation of random effects in early-phase trials-an important consideration for planning subsequent studies. The manuscript illustrates the importance of multiple-sequence designs by analysing data obtainable from a paired and AB/BA cross-over design for a normally distributed variable. It reveals that the residual mean square error from these two designs serves as an unbiased estimator of within-subject variability only under the rare conditions of no subject-by-treatment interaction and equal variances in both test and reference treatments. This implies that while paired or AB/BA cross-over design might be suitable for early pharmacological studies, it should not be relied upon solely for sample size calculations in late-stage studies due to its limited interpretative potential.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70088"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147468863","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}

引用次数: 0

Using Off-Treatment Sequential Multiple Imputation for Binary Outcomes to Address Intercurrent Events Handled by a Treatment Policy Strategy. 用非治疗序贯多重归算二元结果来处理由治疗策略策略处理的并发事件。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70070

Sunita Rehal, Nicky Best, Sarah Watts, Thomas Drury

{"title":"Using Off-Treatment Sequential Multiple Imputation for Binary Outcomes to Address Intercurrent Events Handled by a Treatment Policy Strategy.","authors":"Sunita Rehal, Nicky Best, Sarah Watts, Thomas Drury","doi":"10.1002/pst.70070","DOIUrl":"10.1002/pst.70070","url":null,"abstract":"The estimand framework proposes different strategies to address intercurrent events. The treatment policy strategy seems to be the most favoured as it is closely aligned with the pre-addendum intention-to-treat principle. All data for all patients should ideally be collected; however, in reality patients may withdraw from a study leading to missing data. This needs to be dealt with as part of the estimation. A common intercurrent event we focus on is treatment discontinuation. Several areas of research have been conducted exploring models to estimate the estimand when intercurrent events are handled using a treatment policy strategy; however, the research is limited for binary endpoints. We explore different retrieved dropout models, where post-intercurrent event, the observed data can be used to multiply impute the missing post-intercurrent event data. We compare our proposed models to a simple imputation model that makes no distinction between the pre- and post-intercurrent event data, and assess varying statistical properties through a simulation study. We then provide an example of how retrieved dropout models were used in practice for Phase 3 clinical trials in rheumatoid arthritis. From the models explored, we conclude that a simple retrieved dropout model including an indicator for whether or not the intercurrent event occurred is the most pragmatic choice. However, at least 50% of observed post-intercurrent event data is required for these models to work well. Therefore, the suitability of implementing this model in practice will depend on the amount of observed post-intercurrent event data available and missing data.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"25 2","pages":"e70070"},"PeriodicalIF":1.5,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146132783","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}

引用次数: 0

"Within-Trial" Prognostic Score Adjustment Is Targeted Maximum Likelihood Estimation. “试验内”预后评分调整是针对最大似然估计。

IF 1.5 4区医学

Pharmaceutical Statistics Pub Date : 2026-03-01 DOI: 10.1002/pst.70080

Emilie Højbjerre-Frandsen, Alejandro Schuler

引用次数: 0