Pharmaceutical Statistics最新文献_第5页

Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control. 过度分散泊松数据的预测区间及其在医疗和临床前质量控制中的应用

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-10-30 DOI: 10.1002/pst.2447

Max Menssen, Martina Dammann, Firas Fneish, David Ellenberger, Frank Schaarschmidt

{"title":"Prediction Intervals for Overdispersed Poisson Data and Their Application in Medical and Pre-Clinical Quality Control.","authors":"Max Menssen, Martina Dammann, Firas Fneish, David Ellenberger, Frank Schaarschmidt","doi":"10.1002/pst.2447","DOIUrl":"10.1002/pst.2447","url":null,"abstract":"In pre-clinical and medical quality control, it is of interest to assess the stability of the process under monitoring or to validate a current observation using historical control data. Classically, this is done by the application of historical control limits (HCL) graphically displayed in control charts. In many applications, HCL are applied to count data, for example, the number of revertant colonies (Ames assay) or the number of relapses per multiple sclerosis patient. Count data may be overdispersed, can be heavily right-skewed and clusters may differ in cluster size or other baseline quantities (e.g., number of petri dishes per control group or different length of monitoring times per patient). Based on the quasi-Poisson assumption or the negative-binomial distribution, we propose prediction intervals for overdispersed count data to be used as HCL. Variable baseline quantities are accounted for by offsets. Furthermore, we provide a bootstrap calibration algorithm that accounts for the skewed distribution and achieves equal tail probabilities. Comprehensive Monte-Carlo simulations assessing the coverage probabilities of eight different methods for HCL calculation reveal, that the bootstrap calibrated prediction intervals control the type-1-error best. Heuristics traditionally used in control charts (e.g., the limits in Shewhart c- or u-charts or the mean ± 2 SD) fail to control a pre-specified coverage probability. The application of HCL is demonstrated based on data from the Ames assay and for numbers of relapses of multiple sclerosis patients. The proposed prediction intervals and the algorithm for bootstrap calibration are publicly available via the R package predint.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2447"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11889705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142546680","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

Flexible Spline Models for Blinded Sample Size Reestimation in Event-Driven Clinical Trials. 事件驱动临床试验中盲法样本量重估的灵活样条模型。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-03-01 Epub Date: 2024-12-11 DOI: 10.1002/pst.2459

Tim Mori, Sho Komukai, Satoshi Hattori, Tim Friede

{"title":"Flexible Spline Models for Blinded Sample Size Reestimation in Event-Driven Clinical Trials.","authors":"Tim Mori, Sho Komukai, Satoshi Hattori, Tim Friede","doi":"10.1002/pst.2459","DOIUrl":"10.1002/pst.2459","url":null,"abstract":"In event-driven trials, the target power under a certain treatment effect is maintained as long as the required number of events is obtained. The misspecification of the survival function in the planning phase does not result in a loss of power. However, the trial might take longer than planned if the event rate is lower than assumed. Blinded sample size reestimation (BSSR) uses non-comparative interim data to adjust the sample size if some planning assumptions are wrong. In the setting of an event-driven trial, the sample size may be adjusted to maintain the chances to obtain the required number of events within the planned time frame. For the purpose of BSSR, the survival function is estimated based on the interim data and often needs to be extrapolated. The current practice is to fit standard parametric models, which may however not always be suitable. Here we propose a flexible spline-based BSSR method. Specifically, we propose to carry out the extrapolation based on the Royston-Parmar spline model. To compare the proposed procedure with parametric approaches, we carried out a simulation study. Although parametric approaches might seriously over- or underestimate the expected number of events, the proposed flexible approach avoided such undesirable behavior. This is also observed in an application to a secondary progressive multiple sclerosis trial. Overall, if planning assumptions are wrong this more robust flexible BSSR method could help event-driven designs to more accurately adjust recruitment numbers and to finish on time.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2459"},"PeriodicalIF":1.3,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893375/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142807581","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

Principles for Defining Estimands in Clinical Trials-A Proposal. 定义临床试验估算值的原则--一项建议。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 Epub Date: 2024-08-13 DOI: 10.1002/pst.2432

Tobias Mütze, James Bell, Stefan Englert, Philip Hougaard, Dan Jackson, Vivian Lanius, Henrik Ravn

{"title":"Principles for Defining Estimands in Clinical Trials-A Proposal.","authors":"Tobias Mütze, James Bell, Stefan Englert, Philip Hougaard, Dan Jackson, Vivian Lanius, Henrik Ravn","doi":"10.1002/pst.2432","DOIUrl":"10.1002/pst.2432","url":null,"abstract":"The ICH E9(R1) guideline outlines the estimand framework, which aligns planning, design, conduct, analysis, and interpretation of a clinical trial. The benefits and value of using this framework in clinical trials have been outlined in the literature, and guidance has been provided on how to choose the estimand and define the estimand attributes. Although progress has been made in the implementation of estimands in clinical trials, to the best of our knowledge, there is no published discussion on the basic principles that estimands in clinical trials should fulfill to be well defined and consistent with the ideas presented in the ICH E9(R1) guideline. Therefore, in this Viewpoint article, we propose four key principles for defining an estimand. These principles form a basis for well-defined treatment effects that reflect the estimand thinking process. We hope that this Viewpoint will complement ICH E9(R1) and stimulate a discussion on which fundamental properties an estimand in a clinical trial should have and that such discussions will eventually lead to an improved clarity and precision for defining estimands in clinical trials.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2432"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976330","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

Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting. 在真实世界环境中，在没有个体患者数据的情况下，将治疗效果推广到目标人群。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 Epub Date: 2024-09-03 DOI: 10.1002/pst.2435

Hui Quan, Tong Li, Xun Chen, Gang Li

{"title":"Generalizing Treatment Effect to a Target Population Without Individual Patient Data in a Real-World Setting.","authors":"Hui Quan, Tong Li, Xun Chen, Gang Li","doi":"10.1002/pst.2435","DOIUrl":"10.1002/pst.2435","url":null,"abstract":"The innovative use of real-world data (RWD) can answer questions that cannot be addressed using data from randomized clinical trials (RCTs). While the sponsors of RCTs have a central database containing all individual patient data (IPD) collected from trials, analysts of RWD face a challenge: regulations on patient privacy make access to IPD from all regions logistically prohibitive. In this research, we propose a double inverse probability weighting (DIPW) approach for the analysis sponsor to estimate the population average treatment effect (PATE) for a target population without the need to access IPD. One probability weighting is for achieving comparable distributions in confounders across treatment groups; another probability weighting is for generalizing the result from a subpopulation of patients who have data on the endpoint to the whole target population. The likelihood expressions for propensity scores and the DIPW estimator of the PATE can be written to only rely on regional summary statistics that do not require IPD. Our approach hinges upon the positivity and conditional independency assumptions, prerequisites to most RWD analysis approaches. Simulations are conducted to compare the performances of the proposed method against a modified meta-analysis and a regular meta-analysis.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2435"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142126379","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

Bayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART). 小n次连续多分配随机试验（snSMART）中的贝叶斯样本量计算。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 DOI: 10.1002/pst.2465

Fang Fang, Roy N Tamura, Thomas M Braun, Kelley M Kidwell

{"title":"Bayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART).","authors":"Fang Fang, Roy N Tamura, Thomas M Braun, Kelley M Kidwell","doi":"10.1002/pst.2465","DOIUrl":"10.1002/pst.2465","url":null,"abstract":"A recent study design for clinical trials with small sample sizes is the small n, sequential, multiple assignment, randomized trial (snSMART). An snSMART design has been previously proposed to compare the efficacy of two dose levels versus placebo. In such a trial, participants are initially randomized to receive either low dose, high dose or placebo in stage 1. In stage 2, participants are re-randomized to either dose level depending on their initial treatment and a dichotomous response. A Bayesian analytic approach borrowing information from both stages was proposed and shown to improve the efficiency of estimation. In this paper, we propose two sample size determination (SSD) methods for the proposed snSMART comparing two dose levels with placebo. Both methods adopt the average coverage criterion (ACC) approach. In the first approach, the sample size is calculated in one step, taking advantage of the explicit posterior variance of the treatment effect. In the other two step approach, we update the sample size needed for a single-stage parallel design with a proposed adjustment factor (AF). Through simulations, we demonstrate that the required sample sizes calculated using the two SSD approaches both provide the desired power. We also provide an applet to allow for convenient and fast sample size calculation in this snSMART setting.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 1","pages":"e2465"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024332","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

An Adaptive Three-Arm Comparative Clinical Endpoint Bioequivalence Study Design With Unblinded Sample Size Re-Estimation and Optimized Allocation Ratio. 采用非盲样本量再估计和优化分配比例的自适应三臂临床终点生物等效性比较研究设计

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 Epub Date: 2024-10-08 DOI: 10.1002/pst.2439

David Hinds, Wanjie Sun

{"title":"An Adaptive Three-Arm Comparative Clinical Endpoint Bioequivalence Study Design With Unblinded Sample Size Re-Estimation and Optimized Allocation Ratio.","authors":"David Hinds, Wanjie Sun","doi":"10.1002/pst.2439","DOIUrl":"10.1002/pst.2439","url":null,"abstract":"A three-arm comparative clinical endpoint bioequivalence (BE) study is often used to establish bioequivalence (BE) between a locally acting generic drug (T) and reference drug (R), where superiority needs to be established for T and R over Placebo (P) and equivalence needs to be established for T vs. R. Sometimes, when study design parameters are uncertain, a fixed design study may be under- or over-powered and result in study failure or unnecessary cost. In this paper, we propose a two-stage adaptive clinical endpoint BE study with unblinded sample size re-estimation, standard or maximum combination method, optimized allocation ratio, optional re-estimation of the effect size based on likelihood estimation, and optional re-estimation of the R and P treatment means at interim analysis, which have not been done previously. Our proposed method guarantees control of Type 1 error rate analytically. It helps to reduce the average sample size when the original fixed design is overpowered and increases the sample size and power when the original study and group sequential design are under-powered. Our proposed adaptive design can help generic drug sponsors cut cost and improve success rate, making clinical study endpoint BE studies more affordable and more generic drugs accessible to the public.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"e2439"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142392402","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

Quality by Design for Preclinical In Vitro Assay Development. 临床前体外检测开发的设计质量。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 Epub Date: 2024-09-24 DOI: 10.1002/pst.2430

Jonathan Jones, Bairu Zhang, Xiang Zhang, Peter Konings, Pia Hansson, Anna Backmark, Alessia Serrano, Ulrike Künzel, Steven Novick

引用次数: 0

A Commensurate Prior Model With Random Effects for Survival and Competing Risk Outcomes to Accommodate Historical Controls. 适应历史控制的具有生存和竞争风险结果随机效应的相称先验模型。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 DOI: 10.1002/pst.2464

Manoj Khanal, Brent R Logan, Anjishnu Banerjee, Xi Fang, Kwang Woo Ahn

引用次数: 0

The partnership between statisticians and the Institutional Animal Care and Use Committee (IACUC). 统计人员与机构动物护理和使用委员会（IACUC）之间的合作。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 Epub Date: 2024-06-11 DOI: 10.1002/pst.2390

David Potter, Thomas Bradstreet, Davit Sargsyan, Xiao Tan, Vinicius Bonato, Dingzhou Li, John Liang, Ondrej Libiger, Jocelyn Sendecki, John Stansfield, Kanaka Tatikola, Jialin Xu, Brandy Campbell

引用次数: 0

Introduction to qualification and validation of an immunoassay. 免疫测定的鉴定和验证简介。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 Epub Date: 2024-02-13 DOI: 10.1002/pst.2370

Sarah Janssen

引用次数: 0