Pharmaceutical Statistics最新文献_第9页

Visualizing hypothesis tests in survival analysis under anticipated delayed effects. 预期延迟效应下生存分析中的可视化假设检验

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-05-06 DOI: 10.1002/pst.2393

José L Jiménez, Isobel Barrott, Francesca Gasperoni, Dominic Magirr

{"title":"Visualizing hypothesis tests in survival analysis under anticipated delayed effects.","authors":"José L Jiménez, Isobel Barrott, Francesca Gasperoni, Dominic Magirr","doi":"10.1002/pst.2393","DOIUrl":"10.1002/pst.2393","url":null,"abstract":"What can be considered an appropriate statistical method for the primary analysis of a randomized clinical trial (RCT) with a time-to-event endpoint when we anticipate non-proportional hazards owing to a delayed effect? This question has been the subject of much recent debate. The standard approach is a log-rank test and/or a Cox proportional hazards model. Alternative methods have been explored in the statistical literature, such as weighted log-rank tests and tests based on the Restricted Mean Survival Time (RMST). While weighted log-rank tests can achieve high power compared to the standard log-rank test, some choices of weights may lead to type-I error inflation under particular conditions. In addition, they are not linked to a mathematically unambiguous summary measure. Test statistics based on the RMST, on the other hand, allow one to investigate the average difference between two survival curves up to a pre-specified time point <math><mrow><mi>τ</mi></mrow> </math> -a mathematically unambiguous summary measure. However, by emphasizing differences prior to <math><mrow><mi>τ</mi></mrow> </math> , such test statistics may not fully capture the benefit of a new treatment in terms of long-term survival. In this article, we introduce a graphical approach for direct comparison of weighted log-rank tests and tests based on the RMST. This new perspective allows a more informed choice of the analysis method, going beyond power and type I error comparison.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"870-883"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140859909","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

Using an early outcome as the sole source of information of interim decisions regarding treatment effect on a long-term endpoint: The non-Gaussian case. 将早期结果作为临时决定对长期终点治疗效果的唯一信息来源：非高斯情况

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-06-05 DOI: 10.1002/pst.2398

Leandro Garcia Barrado, Tomasz Burzykowski

引用次数: 0

A model-assisted design for partially or completely ordered groups. 部分或完全有序群体的模型辅助设计。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-05-20 DOI: 10.1002/pst.2396

Connor Celum, Mark Conaway

引用次数: 0

Variable Duration Trial as an Alternative Design for Continuous Endpoints. 可变持续时间试验作为连续终点的替代设计

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-11 DOI: 10.1002/pst.2418

Jitendra Ganju, Julie Guoguang Ma

{"title":"Variable Duration Trial as an Alternative Design for Continuous Endpoints.","authors":"Jitendra Ganju, Julie Guoguang Ma","doi":"10.1002/pst.2418","DOIUrl":"10.1002/pst.2418","url":null,"abstract":"Clinical trials with continuous primary endpoints typically measure outcomes at baseline, at a fixed timepoint (denoted T min), and at intermediate timepoints. The analysis is commonly performed using the mixed model repeated measures method. It is sometimes expected that the effect size will be larger with follow-up longer than T min. But extending the follow-up for all patients delays trial completion. We propose an alternative trial design and analysis method that potentially increases statistical power without extending the trial duration or increasing the sample size. We propose following the last enrolled patient until T min, with earlier enrollees having variable follow-up durations up to a maximum of T max. The sample size at T max will be smaller than at T min, and due to staggered enrollment, data missing at T max will be missing completely at random. For analysis, we propose an alpha-adjusted procedure based on the smaller of the p values at T min and T max, termed <math> <semantics><mrow><mtext>minP</mtext></mrow> </semantics> </math> . This approach can provide the highest power when the powers at T min and T max are similar. If the power at T min and T max differ significantly, the power of <math> <semantics><mrow><mtext>minP</mtext></mrow> </semantics> </math> is modestly reduced compared with the larger of the two powers. Rare disease trials, due to the limited size of the patient population, may benefit the most with this design.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1059-1064"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591009","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

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 : 2024-11-01 Epub Date: 2024-07-16 DOI: 10.1002/pst.2407

Derek Dinart, Virginie Rondeau, Carine Bellera

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 : 2024-11-01 Epub 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

引用次数: 0

Do You Want to Stay Single? Considerations on Single-Arm Trials in Drug Development and the Postregulatory Space. 您想继续单臂试验吗？药物开发中的单臂试验和后监管空间的考虑。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-06-25 DOI: 10.1002/pst.2412

Yulia Dyachkova, Cornelia Dunger-Baldauf, Nathalie Barbier, Jenny Devenport, Stefan Franzén, Gbenga Kazeem, Thomas Künzel, Pierre Mancini, Giacomo Mordenti, Knut Richert, Antonia Ridolfi, Daniel Saure

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 : 2024-11-01 Epub 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":"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":" ","pages":"1095-1116"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602953/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Exploring Stratification Strategies for Population- Versus Randomization-Based Inference. 探索基于人群与随机推断的分层策略。

IF 1.3 4区医学

Pharmaceutical Statistics Pub Date : 2024-11-01 Epub Date: 2024-07-10 DOI: 10.1002/pst.2419

Marco Novelli, William F Rosenberger

{"title":"Exploring Stratification Strategies for Population- Versus Randomization-Based Inference.","authors":"Marco Novelli, William F Rosenberger","doi":"10.1002/pst.2419","DOIUrl":"10.1002/pst.2419","url":null,"abstract":"Stratification on important variables is a common practice in clinical trials, since ensuring cosmetic balance on known baseline covariates is often deemed to be a crucial requirement for the credibility of the experimental results. However, the actual benefits of stratification are still debated in the literature. Other authors have shown that it does not improve efficiency in large samples and improves it only negligibly in smaller samples. This paper investigates different subgroup analysis strategies, with a particular focus on the potential benefits in terms of inferential precision of prestratification versus both poststratification and post hoc regression adjustment. For each of these approaches, the pros and cons of population-based versus randomization-based inference are discussed. The effects of the presence of a treatment-by-covariate interaction and the variability in the patient responses are also taken into account. Our results show that, in general, prestratifying does not provide substantial benefit. On the contrary, it may be deleterious, in particular for randomization-based procedures in the presence of a chronological bias. Even when there is treatment-by-covariate interaction, prestratification may backfire by considerably reducing the inferential precision.","PeriodicalId":19934,"journal":{"name":"Pharmaceutical Statistics","volume":" ","pages":"1045-1058"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11602877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141580473","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

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 : 2024-11-01 Epub 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":"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":" ","pages":"1128-1143"},"PeriodicalIF":1.3,"publicationDate":"2024-11-01","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