Pharmaceutical Statistics最新文献

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

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

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

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

A Commensurate Prior Model With Random Effects for Survival and Competing Risk Outcomes to Accommodate Historical Controls.

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

{"title":"A Commensurate Prior Model With Random Effects for Survival and Competing Risk Outcomes to Accommodate Historical Controls.","authors":"Manoj Khanal, Brent R Logan, Anjishnu Banerjee, Xi Fang, Kwang Woo Ahn","doi":"10.1002/pst.2464","DOIUrl":"https://doi.org/10.1002/pst.2464","url":null,"abstract":"Clinical trials (CTs) often suffer from small sample sizes due to limited budgets and patient enrollment challenges. Using historical data for the CT data analysis may boost statistical power and reduce the required sample size. Existing methods on borrowing information from historical data with right-censored outcomes did not consider matching between historical data and CT data to reduce the heterogeneity. In addition, they studied the survival outcome only, not competing risk outcomes. Therefore, we propose a clustering-based commensurate prior model with random effects for both survival and competing risk outcomes that effectively borrows information based on the degree of comparability between historical and CT data. Simulation results show that the proposed method controls type I errors better and has a lower bias than some competing methods. We apply our method to a phase III CT which compares the effectiveness of bone marrow donated from family members with only partially matched bone marrow versus two partially matched cord blood units to treat leukemia and lymphoma.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 1","pages":"e2464"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143024331","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).

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":"https://doi.org/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

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

Tutorial on Firth's Logistic Regression Models for Biomarkers in Preclinical Space. 临床前生物标记物的 Firth Logistic 回归模型教程。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 Epub Date: 2024-08-06 DOI: 10.1002/pst.2422

Gina D'Angelo, Di Ran

引用次数: 0

Sample Size Estimation for Correlated Count Data With Changes in Dispersion.

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2025-01-01 DOI: 10.1002/pst.2469

Jintong Hou, Leslie A McClure, Savina Jaeger, Lucy F Robinson

{"title":"Sample Size Estimation for Correlated Count Data With Changes in Dispersion.","authors":"Jintong Hou, Leslie A McClure, Savina Jaeger, Lucy F Robinson","doi":"10.1002/pst.2469","DOIUrl":"https://doi.org/10.1002/pst.2469","url":null,"abstract":"Clinical endpoints based on repeated measurements arise in many clinical research studies, and require specialized methods for sample size and power calculations. In clinical trials that measure counts over time, such as bleeding events in hemophilia, the dispersion of their distributions might change upon treatment and the measurements might be correlated. The generalized estimating equations (GEE) approach has been widely used for modeling correlated data and comparing rates. In this paper, we investigate the properties of GEE when applied to count outcomes with changes in dispersion. We derive general closed-form formulas to estimate sample size when the dispersion parameters and distributions of count data vary across two correlated measurements based on the GEE approach. These formulas allow for power and sample size estimation for intra-participant comparison of rates before and after an intervention, randomized controlled trials with equal allocation, or matched pairs designs. These formulas are derived for the following distributions: Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial distributions, and do not assume that measurements before and after an intervention come from the same distribution. Furthermore, we propose modified methods for estimating sample size and confidence intervals for the negative binomial distributions to overcome Type I error inflation, which is especially useful for large changes in the negative binomial dispersion parameter. We perform simulations, and evaluate the performance of the empirical power and Type I error over a range of parameters. Applications and R functions implementing the methods are also provided.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":"24 1","pages":"e2469"},"PeriodicalIF":1.3,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143189784","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