Hajime Uno,Lu Tian,Miki Horiguchi et al.
Hajime Uno et al.
Target Aggregate Data Adjustment Method for Transportability Analysis Utilizing Summary-Level Data From the Target Population [0.03%]
利用目标人群汇总数据进行外推性的靶标汇总数据分析的调整方法
Yichen Yan,Quang Vuong,Rebecca K Metcalfe et al.
Yichen Yan et al.
Transportability analysis is a causal inference framework used to evaluate the external validity of studies by transporting treatment effects from a study sample to an external target population by adjusting for differences in the distribut...
A Flexible Seamless Phase 2/3 Design With Biomarker-Driven Subgroup Enrichment and Sample Size Re-Estimation [0.03%]
一种具有生物标志物亚组富集和样本量重新估计的灵活连续二期三期设计
Zizhong Tian,Liwen Wu,Rachael Liu et al.
Zizhong Tian et al.
To support the expedited drug development addressing unmet medical needs, the seamless phase 2/3 design that makes the phase switching decision based on early surrogate endpoints is gaining popularity in practice. For also catering to poten...
A Tutorial on Improving RCT Power Using Prognostic Score Adjustment for Linear Models [0.03%]
基于线性模型利用预后分数调整提升随机对照试验功效的教程
Emilie Højbjerre-Frandsen,Mathias Lerbech Jeppesen,Rasmus Kuhr Jensen et al.
Emilie Højbjerre-Frandsen et al.
The use of historical data to increase power in clinical trials has been a topic of interest for many years. A recent approach adjusts linearly for a prognostic score. This is supported by asymptotic optimality results using influence funct...
Mediation Analysis of Path-Specific Effects in Randomised Clinical Trials With Repeatedly Measured Mediators and Outcomes [0.03%]
随机临床试验中重复测量中介变量和结果的路径特异效应的介导分析
Martin Linder,Jesper Madsen,Stijn Vansteelandt
Martin Linder
Questions about the mode of action (MoA) of a drug have interest to scientific communities as well as regulatory authorities. In the absence of an already established MoA such questions may be enlightened by causal mediation analysis in cli...
Sample Size for Enriched Biomarker Designs With Measurement Error for Time-to-Event Outcomes [0.03%]
具有测量误差的生存结果的生物标志物富集设计的样本量计算方法研究
Siyuan Guo,Susan Halabi,Aiyi Liu
Siyuan Guo
A major emphasis in personalized medicine is to optimally treat subgroups of patients who may benefit from certain therapeutic agents. One relevant study design is the targeted design, in which patients have consented for their specimens to...
A General Approach for Sample Size Calculation With Nonproportional Hazards and Cure Rates [0.03%]
具有非比例风险和治愈率的样本量计算的通用方法
Huan Cheng,Xiaoyun Li,Jianghua He
Huan Cheng
With the ongoing advancements in cancer drug development, a subset of patients can live quite long, or are even considered cured in certain cancer types. Additionally, nonproportional hazards, such as delayed treatment effects and crossing ...
Estimating the Variance of Covariate-Adjusted Estimators of Average Treatment Effects in Clinical Trials With Binary Endpoints [0.03%]
基于二分类终点的临床试验中平均处理效应的协变量调整估计方差的估计方法研究
Dominic Magirr,Craig Wang,Alexander Przybylski et al.
Dominic Magirr et al.
Covariate-adjusted estimators of average treatment effects in clinical trials are typically more efficient than unadjusted estimators. Recent guidance from the FDA is highly detailed regarding the appropriate use of covariate adjustment for...
Personalized Treatment Selection for Multivariate Ordinal Scale Outcomes and Multiple Treatments [0.03%]
基于多元有序评分结果和多种治疗方法的选择个性化治疗方案
Chathura Siriwardhana,Bakeerathan Gunaratnam,K B Kulasekera
Chathura Siriwardhana
In this study, we present an innovative approach for tailoring treatment selection on an individualized basis in the presence of correlated multiple responses, particularly those measured on ordinal scales, including binary responses. Our m...
Further Practical Guidance on Adjusting Time-To-Event Outcomes for Treatment Switching [0.03%]
治疗转换调整时间依赖性结局的进一步实用指导建议
Claire Watkins,Eva Kleine,Miguel Miranda et al.
Claire Watkins et al.
The objective of this article is to bring together the key current information on practical considerations when conducting statistical analyses adjusting long-term outcomes for treatment switching, combining it with learnings from our own e...