Pharmaceutical Statistics最新文献_第2页

Detection of Outlying Correlation Coefficients in Multicenter Clinical Trials. 多中心临床试验中离群相关系数的检测。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-05-01 DOI: 10.1002/pst.70013

Lieven Desmet, David Venet, Laura Trotta, Tomasz Burzykowski, Marc Buyse

{"title":"Detection of Outlying Correlation Coefficients in Multicenter Clinical Trials.","authors":"Lieven Desmet, David Venet, Laura Trotta, Tomasz Burzykowski, Marc Buyse","doi":"10.1002/pst.70013","DOIUrl":"10.1002/pst.70013","url":null,"abstract":"Central statistical monitoring aims at finding centers whose data distribution differs significantly from the other centers in multicentric clinical trials. Such differences may point to data quality issues due to negligence, misconduct, or fraud. Data distributions can be compared across centers in many different ways, depending on the type of data (e.g., numerical or categorical), whether a univariate or a multivariate comparison is performed, and so on. In that framework, we present two methods aimed at detecting centers with outlying bivariate Pearson correlation coefficients. One of the methods directly compares the correlations across centers. The other method conditions the test on one of the marginal standard deviations, which makes the test on correlation independent of the centers' standard deviations. Both methods are shown to perform equally well on simulated data. They are also applied on real world data, where they identify centers with outlying correlations. The findings of the two tests are compared, showing that they concord for centers with average standard deviations, but differ for centers with extreme standard deviations. While the focus here is on central statistical monitoring, the methods are general and can be used in other settings.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 3","pages":"e70013"},"PeriodicalIF":1.3,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754108","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

Correction to "The Flaw of Averages: Bayes Factors as Posterior Means of the Likelihood Ratio". 修正“平均的缺陷：贝叶斯因子作为似然比的后验均值”。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-03 DOI: 10.1002/pst.2441

引用次数: 0

Beyond the Fragility Index. 超越脆弱性指数。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-21 DOI: 10.1002/pst.2452

Piero Quatto, Enrico Ripamonti, Donata Marasini

{"title":"Beyond the Fragility Index.","authors":"Piero Quatto, Enrico Ripamonti, Donata Marasini","doi":"10.1002/pst.2452","DOIUrl":"10.1002/pst.2452","url":null,"abstract":"The results of randomized clinical trials (RCTs) are frequently assessed with the fragility index (FI). Although the information provided by FI may supplement the p value, this indicator presents intrinsic weaknesses and shortcomings. In this article, we establish an analysis of fragility within a broader framework so that it can reliably complement the information provided by the p value. This perspective is named the analysis of strength. We first propose a new strength index (SI), which can be adopted in normal distribution settings. This measure can be obtained for both significance and nonsignificance and is straightforward to calculate, thus presenting compelling advantages over FI, starting from the presence of a threshold. The case of time-to-event outcomes is also addressed. Then, beyond the p value, we develop the analysis of strength using likelihood ratios from Royall's statistical evidence viewpoint. A new R package is provided for performing strength calculations, and a simulation study is conducted to explore the behavior of SI and the likelihood-based indicator empirically across different settings. The newly proposed analysis of strength is applied in the assessment of the results of three recent trials involving the treatment of COVID-19.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2452"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889990/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142687990","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

Subgroup Identification Based on Quantitative Objectives. 基于量化目标的分组识别。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-11-17 DOI: 10.1002/pst.2455

Yan Sun, A S Hedayat

引用次数: 0

A Phase I Dose-Finding Design Incorporating Intra-Patient Dose Escalation. 纳入患者内剂量递增的I期剂量寻找设计。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-12-25 DOI: 10.1002/pst.2461

Beibei Guo, Suyu Liu

{"title":"A Phase I Dose-Finding Design Incorporating Intra-Patient Dose Escalation.","authors":"Beibei Guo, Suyu Liu","doi":"10.1002/pst.2461","DOIUrl":"10.1002/pst.2461","url":null,"abstract":"Conventional Phase I trial designs assign a single dose to each patient, necessitating a minimum number of patients per dose to reliably identify the maximum tolerated dose (MTD). However, in many clinical trials, such as those involving pediatric patients or patients with rare cancers, recruiting an adequate number of patients can pose challenges, limiting the applicability of standard trial designs. To address this challenge, we propose a new Phase I dose-finding design, denoted as IP-CRM, that integrates intra-patient dose escalation with the continual reassessment method (CRM). In the IP-CRM design, intra-patient dose escalation is allowed, guided by both individual patients' toxicity outcomes and accumulated data across patients, and the starting dose for each cohort of patients is adaptively updated. We further extend the IP-CRM design to address carryover effects and/or intra-patient correlations. Due to the potential for each patient to contribute multiple data points at varying doses owing to intra-patient dose escalation, the IP-CRM design offers the advantage of determining the MTD with a considerably reduced sample size compared to standard Phase I dose-finding designs. Simulation studies show that our IP-CRM design can efficiently reduce sample size while concurrently enhancing the probability of identifying the MTD when compared with standard CRM designs and the 3 + 3 design.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2461"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142896374","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

Approximate Bayesian Analysis for Borrowing External Controls for Randomized Controlled Trials With Dynamic Borrowing and Covariate Balancing Adjustment. 动态借用和协变量平衡调整随机对照试验借用外部对照的近似贝叶斯分析。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 DOI: 10.1002/pst.2474

Jixian Wang, Ram Tiwari

{"title":"Approximate Bayesian Analysis for Borrowing External Controls for Randomized Controlled Trials With Dynamic Borrowing and Covariate Balancing Adjustment.","authors":"Jixian Wang, Ram Tiwari","doi":"10.1002/pst.2474","DOIUrl":"10.1002/pst.2474","url":null,"abstract":"Borrowing controls from external sources has become popular for augmenting the control arm in small randomized controlled trials (RCTs). Due to the difference between the external and RCT populations, bias can be introduced that may lead to invalid statistical inference based on combined data. To mitigate this risk, dynamic borrowing which adaptively determines the amount of borrowing, can be used together with pre-adjustment for prognostic factors in the external data. To take into account the variability due to the estimation of the amount of borrowing and the pre-adjustment, we propose a Bayesian bootstrap (BB)-based integrated Bayesian approach together with covariate balancing (CB) for pre-adjustment. We show that the proposed BB based approach is a valid approximate Bayesian approach with CB using different distances, particularly Euclidean or entropy distance. This justification is not trivial because CB has a different nature from the probability-based approach. We also propose a BB-algorithm for generating an approximate posterior sample, which is easy to implement and computationally efficient. Statistical inference for estimand of interest using combined external and internal data can be based on the bootstrapped posterior sample or on an approximate normal distribution with parameters estimated by BB. To examine the properties of the proposed approach, we conduct an extensive simulation study. The approach is illustrated by borrowing controls for an acute myeloid leukemia trial from another study.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 2","pages":"e2474"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143503174","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

A Bayesian Hybrid Design With Borrowing From Historical Study. 借鉴历史研究的贝叶斯混合设计。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-12-27 DOI: 10.1002/pst.2466

Zhaohua Lu, John Toso, Girma Ayele, Philip He

{"title":"A Bayesian Hybrid Design With Borrowing From Historical Study.","authors":"Zhaohua Lu, John Toso, Girma Ayele, Philip He","doi":"10.1002/pst.2466","DOIUrl":"10.1002/pst.2466","url":null,"abstract":"In early phase drug development of combination therapy, the primary objective is to preliminarily assess whether there is additive activity from a novel agent when combined with an established monotherapy. Due to potential feasibility issues for conducting a large randomized study, uncontrolled single-arm trials have been the mainstream approach in cancer clinical trials. However, such trials often present significant challenges in deciding whether to proceed to the next phase of development due to the lack of randomization in traditional two-arm trials. A hybrid design, leveraging data from a completed historical clinical study of the monotherapy, offers a valuable option to enhance study efficiency and improve informed decision-making. Compared to traditional single-arm designs, the hybrid design may significantly enhance power by borrowing external information, enabling a more robust assessment of activity. The primary challenge of hybrid design lies in handling information borrowing. We introduce a Bayesian dynamic power prior (DPP) framework with three components of controlling amount of dynamic borrowing. The framework offers flexible study design options with explicit interpretation of borrowing, allowing customization according to specific needs. Furthermore, the posterior distribution in the proposed framework has a closed form, offering significant advantages in computational efficiency. The proposed framework's utility is demonstrated through simulations and a case study.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2466"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142896437","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

Confidence Intervals for the Risk Difference Between Secondary and Primary Infection Based on the Method of Variance Estimates Recovery. 基于方差估计方法的继发感染和原发感染风险差异的置信区间。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-12-09 DOI: 10.1002/pst.2458

Chao Chen, Yuanzhen Li, Qitong Wei, Zhigang Huang, Yanting Chen

引用次数: 0

Bayesian Response Adaptive Randomization for Randomized Clinical Trials With Continuous Outcomes: The Role of Covariate Adjustment. 连续结果随机临床试验的贝叶斯反应自适应随机化：协变量调整的作用》。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-10-24 DOI: 10.1002/pst.2443

Vahan Aslanyan, Trevor Pickering, Michelle Nuño, Lindsay A Renfro, Judy Pa, Wendy J Mack

{"title":"Bayesian Response Adaptive Randomization for Randomized Clinical Trials With Continuous Outcomes: The Role of Covariate Adjustment.","authors":"Vahan Aslanyan, Trevor Pickering, Michelle Nuño, Lindsay A Renfro, Judy Pa, Wendy J Mack","doi":"10.1002/pst.2443","DOIUrl":"10.1002/pst.2443","url":null,"abstract":"Study designs incorporate interim analyses to allow for modifications to the trial design. These analyses may aid decisions regarding sample size, futility, and safety. Furthermore, they may provide evidence about potential differences between treatment arms. Bayesian response adaptive randomization (RAR) skews allocation proportions such that fewer participants are assigned to the inferior treatments. However, these allocation changes may introduce covariate imbalances. We discuss two versions of Bayesian RAR (with and without covariate adjustment for a binary covariate) for continuous outcomes analyzed using change scores and repeated measures, while considering either regression or mixed models for interim analysis modeling. Through simulation studies, we show that RAR (both versions) allocates more participants to better treatments compared to equal randomization, while reducing potential covariate imbalances. We also show that dynamic allocation using mixed models for repeated measures yields a smaller allocation proportion variance while having a similar covariate imbalance as regression models. Additionally, covariate imbalance was smallest for methods using covariate-adjusted RAR (CARA) in scenarios with small sample sizes and covariate prevalence less than 0.3. Covariate imbalance did not differ between RAR and CARA in simulations with larger sample sizes and higher covariate prevalence. We thus recommend a CARA approach for small pilot/exploratory studies for the identification of candidate treatments for further confirmatory studies.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2443"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142505735","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

WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors. 观察：临床试验发起者评估药物开发治疗效果异质性的工作流程。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-12-26 DOI: 10.1002/pst.2463

Konstantinos Sechidis, Sophie Sun, Yao Chen, Jiarui Lu, Cong Zhang, Mark Baillie, David Ohlssen, Marc Vandemeulebroecke, Rob Hemmings, Stephen Ruberg, Björn Bornkamp

引用次数: 0