Pharmaceutical Statistics最新文献_第7页

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 : 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":"https://doi.org/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":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-13","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

Bayesian Predictive Probability Based on a Bivariate Index Vector for Single-Arm Phase II Study With Binary Efficacy and Safety Endpoints. 基于双变量指数向量的贝叶斯预测概率，用于具有二元有效性和安全性终点的单臂 II 期研究。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-13 DOI: 10.1002/pst.2431

Takuya Yoshimoto, Satoru Shinoda, Kouji Yamamoto, Kouji Tahata

{"title":"Bayesian Predictive Probability Based on a Bivariate Index Vector for Single-Arm Phase II Study With Binary Efficacy and Safety Endpoints.","authors":"Takuya Yoshimoto, Satoru Shinoda, Kouji Yamamoto, Kouji Tahata","doi":"10.1002/pst.2431","DOIUrl":"https://doi.org/10.1002/pst.2431","url":null,"abstract":"In oncology, Phase II studies are crucial for clinical development plans as such studies identify potent agents with sufficient activity to continue development in the subsequent Phase III trials. Traditionally, Phase II studies are single-arm studies, with the primary endpoint being short-term treatment efficacy. However, drug safety is also an important consideration. In the context of such multiple-outcome designs, predictive probability-based Bayesian monitoring strategies have been developed to assess whether a clinical trial will provide enough evidence to continue with a Phase III study at the scheduled end of the trial. Therefore, we propose a new simple index vector to summarize the results that cannot be captured by existing strategies. Specifically, we define the worst and most promising situations for the potential effect of a treatment, then use the proposed index vector to measure the deviation between the two situations. Finally, simulation studies are performed to evaluate the operating characteristics of the design. The obtained results demonstrate that the proposed method makes appropriate interim go/no-go decisions.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141976329","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

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 : 2024-08-06 DOI: 10.1002/pst.2422

Gina D'Angelo, Di Ran

引用次数: 0

Mixture Experimentation in Pharmaceutical Formulations: A Tutorial. 药物制剂中的混合物实验：教程。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-05 DOI: 10.1002/pst.2426

Lynne B Hare, Stan Altan, Hans Coppenolle

{"title":"Mixture Experimentation in Pharmaceutical Formulations: A Tutorial.","authors":"Lynne B Hare, Stan Altan, Hans Coppenolle","doi":"10.1002/pst.2426","DOIUrl":"https://doi.org/10.1002/pst.2426","url":null,"abstract":"Mixture experimentation is commonly seen in pharmaceutical formulation studies, where the relative proportions of the individual components are modeled for effects on product attributes. The requirement that the sum of the component proportions equals 1 has given rise to the class of designs, known as mixture designs. The first mixture designs were published by Quenouille in 1953 but it took nearly 40 years for the earliest mixture design applications to be published in the pharmaceutical sciences literature by Kettaneh-Wold in 1991 and Waaler in 1992. Since then, the advent of efficient computer algorithms to generate designs has made this class of designs easily accessible to pharmaceutical statisticians, although the use of these designs appears to be an underutilized experimental strategy even today. One goal of this tutorial is to draw the attention of experimental statisticians to this class of designs and their advantages in pursuing formulation studies such as excipient compatibility studies. We present sufficient materials to introduce the novice practitioner to this class of design, associated models, and analysis strategies. An example of a mixture-process variable design is given as a case study.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141894062","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

Strategy for Designing In Vivo Dose-Response Comparison Studies. 设计体内剂量-反应比较研究的策略

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-17 DOI: 10.1002/pst.2421

Steven Novick, Tianhui Zhang

引用次数: 0

The Role of CMC Statisticians: Co-Practitioners of the Scientific Method. CMC 统计人员的作用：科学方法的共同实践者。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-10 DOI: 10.1002/pst.2420

Timothy Schofield

引用次数: 0

Potency Assay Variability Estimation in Practice. 实践中的药效测定变异性估算。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-08 DOI: 10.1002/pst.2408

Hang Li, Tomasz M Witkos, Scott Umlauf, Christopher Thompson

引用次数: 0

A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data. 案例研究：利用生物标志物数据建立复发率预测模型，评估修订剂量方案的疗效。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-05 DOI: 10.1002/pst.2362

Ahrim Youn, Jiarui Chi, Yue Cui, Hui Quan

{"title":"A case study: Assessing the efficacy of the revised dosage regimen via prediction model for recurrent event rate using biomarker data.","authors":"Ahrim Youn, Jiarui Chi, Yue Cui, Hui Quan","doi":"10.1002/pst.2362","DOIUrl":"10.1002/pst.2362","url":null,"abstract":"In recently conducted phase III trials in a rare disease area, patients received monthly treatment at a high dose of the drug, which targets to lower a specific biomarker level, closely associated with the efficacy endpoint, to around 10% across patients. Although this high dose demonstrated strong efficacy, treatments were withheld due to the reports of serious adverse events. Dosing in these studies were later resumed at a reduced dosage which targets to lower the biomarker level to 15%-35% across patients. Two questions arose after this disruption. The first is whether the efficacy of this revised regimen as measured by the reduction in annualized event rate is adequate to support the continuation of the development and the second is whether the potential bias due to the loss of patients during this dosing gap process can be gauged. To address these questions, we built a prediction model that quantitatively characterizes biomarker vs. endpoint relationship and predicts efficacy at the 15%-35% range of the biomarker level using the available data from the original high dose. This model predicts favorable event rate in the target biomarker level and shows that the bias due to the loss of patients is limited. These results support the continued development of the revised regimen, however, given the limitation of the data available, this prediction is planned to be validated further when data under the revised regimen become available.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"570-584"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139692642","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

Predicting subgroup treatment effects for a new study: Motivations, results and learnings from running a data challenge in a pharmaceutical corporation. 预测新研究的亚组治疗效果：在制药公司开展数据挑战的动机、结果和经验。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-07 DOI: 10.1002/pst.2368

Björn Bornkamp, Silvia Zaoli, Michela Azzarito, Ruvie Martin, Carsten Philipp Müller, Conor Moloney, Giulia Capestro, David Ohlssen, Mark Baillie

引用次数: 0

Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence. 评估基于群体的轨迹建模方法在发现不同服药模式方面的性能。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-01 Epub Date: 2024-02-08 DOI: 10.1002/pst.2365

Awa Diop, Alind Gupta, Sabrina Mueller, Louis Dron, Ofir Harari, Heather Berringer, Vinusha Kalatharan, Jay J H Park, Miceline Mésidor, Denis Talbot

{"title":"Assessing the performance of group-based trajectory modeling method to discover different patterns of medication adherence.","authors":"Awa Diop, Alind Gupta, Sabrina Mueller, Louis Dron, Ofir Harari, Heather Berringer, Vinusha Kalatharan, Jay J H Park, Miceline Mésidor, Denis Talbot","doi":"10.1002/pst.2365","DOIUrl":"10.1002/pst.2365","url":null,"abstract":"It is well known that medication adherence is critical to patient outcomes and can decrease patient mortality. The Pharmacy Quality Alliance (PQA) has recognized and identified medication adherence as an important indicator of medication-use quality. Hence, there is a need to use the right methods to assess medication adherence. The PQA has endorsed the proportion of days covered (PDC) as the primary method of measuring adherence. Although easy to calculate, the PDC has however several drawbacks as a method of measuring adherence. PDC is a deterministic approach that cannot capture the complexity of a dynamic phenomenon. Group-based trajectory modeling (GBTM) is increasingly proposed as an alternative to capture heterogeneity in medication adherence. The main goal of this paper is to demonstrate, through a simulation study, the ability of GBTM to capture treatment adherence when compared to its deterministic PDC analogue and to the nonparametric longitudinal K-means. A time-varying treatment was generated as a quadratic function of time, baseline, and time-varying covariates. Three trajectory models are considered combining a cat's cradle effect, and a rainbow effect. The performance of GBTM was compared to the PDC and longitudinal K-means using the absolute bias, the variance, the c-statistics, the relative bias, and the relative variance. For all explored scenarios, we find that GBTM performed better in capturing different patterns of medication adherence with lower relative bias and variance even under model misspecification than PDC and longitudinal K-means.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"511-529"},"PeriodicalIF":1.3,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139703122","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