Pharmaceutical Statistics最新文献_第2页

Applying the Estimand Framework to Non‐Inferiority Trials. 将 Estimand 框架应用于非劣效性试验。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-08 DOI: 10.1002/pst.2433

Helle Lynggaard, Oliver N Keene, Tobias Mütze, Sunita Rehal

{"title":"Applying the Estimand Framework to Non‐Inferiority Trials.","authors":"Helle Lynggaard, Oliver N Keene, Tobias Mütze, Sunita Rehal","doi":"10.1002/pst.2433","DOIUrl":"https://doi.org/10.1002/pst.2433","url":null,"abstract":"Most published applications of the estimand framework have focused on superiority trials. However, non-inferiority trials present specific challenges compared to superiority trials. The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use notes in their addendum on estimands and sensitivity analysis in clinical trials that there may be special considerations to the implementation of estimands in clinical trials with a non-inferiority objective yet provides little guidance. This paper discusses considerations that trial teams should make when defining estimands for a clinical trial with a non-inferiority objective. We discuss how the pre-addendum way of establishing non-inferiority can be embraced by the estimand framework including a discussion of the role of the Per Protocol analysis set. We examine what clinical questions of interest can be formulated in the context of non-inferiority trials and outline why we do not think it is sensible to describe an estimand as 'conservative'. The impact of the estimand framework on key considerations in non-inferiority trials such as whether trials should have more than one primary estimand, the choice of non-inferiority margin, assay sensitivity, switching from non-inferiority to superiority and estimation are discussed. We conclude by providing a list of recommendations, and important considerations for defining estimands for trials with a non-inferiority objective.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141902593","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":null,"pages":null},"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

Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation. 使用非治疗序列多重估算法估算连续结果的治疗政策估计值。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-08-04 DOI: 10.1002/pst.2411

Thomas Drury, Juan J Abellan, Nicky Best, Ian R White

{"title":"Estimation of Treatment Policy Estimands for Continuous Outcomes Using Off-Treatment Sequential Multiple Imputation.","authors":"Thomas Drury, Juan J Abellan, Nicky Best, Ian R White","doi":"10.1002/pst.2411","DOIUrl":"https://doi.org/10.1002/pst.2411","url":null,"abstract":"The estimands framework outlined in ICH E9 (R1) describes the components needed to precisely define the effects to be estimated in clinical trials, which includes how post-baseline 'intercurrent' events (IEs) are to be handled. In late-stage clinical trials, it is common to handle IEs like 'treatment discontinuation' using the treatment policy strategy and target the treatment effect on outcomes regardless of treatment discontinuation. For continuous repeated measures, this type of effect is often estimated using all observed data before and after discontinuation using either a mixed model for repeated measures (MMRM) or multiple imputation (MI) to handle any missing data. In basic form, both these estimation methods ignore treatment discontinuation in the analysis and therefore may be biased if there are differences in patient outcomes after treatment discontinuation compared with patients still assigned to treatment, and missing data being more common for patients who have discontinued treatment. We therefore propose and evaluate a set of MI models that can accommodate differences between outcomes before and after treatment discontinuation. The models are evaluated in the context of planning a Phase 3 trial for a respiratory disease. We show that analyses ignoring treatment discontinuation can introduce substantial bias and can sometimes underestimate variability. We also show that some of the MI models proposed can successfully correct the bias, but inevitably lead to increases in variance. We conclude that some of the proposed MI models are preferable to the traditional analysis ignoring treatment discontinuation, but the precise choice of MI model will likely depend on the trial design, disease of interest and amount of observed and missing data following treatment discontinuation.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141889907","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

Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials. 利用分数多项式进行高效研究设计和纵向剂量反应数据分析

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-28 DOI: 10.1002/pst.2425

Benjamin F Hartley, Dave Lunn, Adrian P Mander

{"title":"Efficient Study Design and Analysis of Longitudinal Dose-Response Data Using Fractional Polynomials.","authors":"Benjamin F Hartley, Dave Lunn, Adrian P Mander","doi":"10.1002/pst.2425","DOIUrl":"https://doi.org/10.1002/pst.2425","url":null,"abstract":"Correctly characterising the dose-response relationship and taking the correct dose forward for further study is a critical part of the drug development process. We use optimal design theory to compare different designs and show that using longitudinal data from all available timepoints in a continuous-time dose-response model can substantially increase the efficiency of estimation of the dose-response compared to a single timepoint model. We give theoretical results to calculate the efficiency gains for a large class of these models. For example, a linearly growing Emax dose-response in a population with a between/within-patient variance ratio ranging from 0.1 to 1 measured at six visits can be estimated with between 1.43 and 2.22 times relative efficiency gain, or equivalently, with 30% to a 55% reduced sample size, compared to a single model of the final timepoint. Fractional polynomials are a flexible way to incorporate data from repeated measurements, increasing precision without imposing strong constraints. Longitudinal dose-response models using two fractional polynomial terms are robust to mis-specification of the true longitudinal process while maintaining, often large, efficiency gains. These models have applications for characterising the dose-response at interim or final analyses.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141788779","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

Reparametrized Firth's Logistic Regressions for Dose-Finding Study With the Biased-Coin Design. 采用偏币设计的剂量寻找研究中的重拟合 Firth Logistic 回归。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-16 DOI: 10.1002/pst.2423

Hyungwoo Kim, Seungpil Jung, Yudi Pawitan, Woojoo Lee

引用次数: 0

Sample Size Estimation Using a Partially Clustered Frailty Model for Biomarker-Strategy Designs With Multiple Treatments. 使用部分聚类虚弱模型估算具有多种治疗方法的生物标记物策略设计的样本量。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-16 DOI: 10.1002/pst.2407

Derek Dinart, Virginie Rondeau, Carine Bellera

引用次数: 0

Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation. 处理治疗退出后的部分观察试验数据：引入以检索到的辍学参考基数为中心的多重估算。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-16 DOI: 10.1002/pst.2416

Suzie Cro, James H Roger, James R Carpenter

{"title":"Handling Partially Observed Trial Data After Treatment Withdrawal: Introducing Retrieved Dropout Reference-Base Centred Multiple Imputation.","authors":"Suzie Cro, James H Roger, James R Carpenter","doi":"10.1002/pst.2416","DOIUrl":"https://doi.org/10.1002/pst.2416","url":null,"abstract":"The ICH E9(R1) Addendum (International Council for Harmonization 2019) suggests treatment-policy as one of several strategies for addressing intercurrent events such as treatment withdrawal when defining an estimand. This strategy requires the monitoring of patients and collection of primary outcome data following termination of randomised treatment. However, when patients withdraw from a study early before completion this creates true missing data complicating the analysis. One possible way forward uses multiple imputation to replace the missing data based on a model for outcome on- and off-treatment prior to study withdrawal, often referred to as retrieved dropout multiple imputation. This article introduces a novel approach to parameterising this imputation model so that those parameters which may be difficult to estimate have mildly informative Bayesian priors applied during the imputation stage. A core reference-based model is combined with a retrieved dropout compliance model, using both on- and off-treatment data, to form an extended model for the purposes of imputation. This alleviates the problem of specifying a complex set of analysis rules to accommodate situations where parameters which influence the estimated value are not estimable, or are poorly estimated leading to unrealistically large standard errors in the resulting analysis. We refer to this new approach as retrieved dropout reference-base centred multiple imputation.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141627325","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 Hierarchical Models for Subgroup Analysis. 用于分组分析的贝叶斯层次模型。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-07-15 DOI: 10.1002/pst.2424

Yun Wang, Wenda Tu, William Koh, James Travis, Robert Abugov, Kiya Hamilton, Mengjie Zheng, Roberto Crackel, Pablo Bonangelino, Mark Rothmann

{"title":"Bayesian Hierarchical Models for Subgroup Analysis.","authors":"Yun Wang, Wenda Tu, William Koh, James Travis, Robert Abugov, Kiya Hamilton, Mengjie Zheng, Roberto Crackel, Pablo Bonangelino, Mark Rothmann","doi":"10.1002/pst.2424","DOIUrl":"https://doi.org/10.1002/pst.2424","url":null,"abstract":"In conventional subgroup analyses, subgroup treatment effects are estimated using data from each subgroup separately without considering data from other subgroups in the same study. The subgroup treatment effects estimated this way may be heterogenous with high variability due to small sample sizes in some subgroups and much different from the treatment effect in the overall population. A Bayesian hierarchical model (BHM) can be used to derive more precise, and less heterogenous estimates of subgroup treatment effects that are closer to the treatment effect in the overall population. BHM assumes exchangeability in treatment effect across subgroups after adjusting for effect modifiers and other relevant covariates. In this article, we will discuss the technical details for applying one-way and multi-way BHM using summary-level statistics, and patient-level data for subgroup analysis. Four case studies based on four new drug applications are used to illustrate the application of these models in subgroup analyses for continuous, dichotomous, time-to-event, and count endpoints.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141620632","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