WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors.

IF 1.3 4区 医学 Q4 PHARMACOLOGY & PHARMACY Pharmaceutical Statistics Pub Date : 2024-12-26 DOI:10.1002/pst.2463
Konstantinos Sechidis, Sophie Sun, Yao Chen, Jiarui Lu, Cong Zhang, Mark Baillie, David Ohlssen, Marc Vandemeulebroecke, Rob Hemmings, Stephen Ruberg, Björn Bornkamp
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Abstract

This article proposes a Workflow for Assessing Treatment effeCt Heterogeneity (WATCH) in clinical drug development targeted at clinical trial sponsors. WATCH is designed to address the challenges of investigating treatment effect heterogeneity (TEH) in randomized clinical trials, where sample size and multiplicity limit the reliability of findings. The proposed workflow includes four steps: analysis planning, initial data analysis and analysis dataset creation, TEH exploration, and multidisciplinary assessment. The workflow offers a general overview of how treatment effects vary by baseline covariates in the observed data and guides the interpretation of the observed findings based on external evidence and the best scientific understanding. The workflow is exploratory and not inferential/confirmatory in nature but should be preplanned before database lock and analysis start. It is focused on providing a general overview rather than a single specific finding or subgroup with a differential effect.

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观察:临床试验发起者评估药物开发治疗效果异质性的工作流程。
本文针对临床试验发起者提出了临床药物开发中评估治疗效果异质性的工作流程(WATCH)。WATCH旨在解决随机临床试验中研究治疗效果异质性(TEH)的挑战,其中样本量和多样性限制了结果的可靠性。提出的工作流程包括四个步骤:分析规划、初始数据分析和分析数据集创建、TEH探索和多学科评估。该工作流程提供了治疗效果如何随观察数据中基线协变量变化的总体概述,并指导根据外部证据和最佳科学理解对观察结果的解释。工作流是探索性的,本质上不是推断/确认性的,但应该在数据库锁定和分析开始之前预先计划好。它侧重于提供总体概述,而不是单个特定的发现或具有不同效果的子组。
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来源期刊
Pharmaceutical Statistics
Pharmaceutical Statistics 医学-统计学与概率论
CiteScore
2.70
自引率
6.70%
发文量
90
审稿时长
6-12 weeks
期刊介绍: Pharmaceutical Statistics is an industry-led initiative, tackling real problems in statistical applications. The Journal publishes papers that share experiences in the practical application of statistics within the pharmaceutical industry. It covers all aspects of pharmaceutical statistical applications from discovery, through pre-clinical development, clinical development, post-marketing surveillance, consumer health, production, epidemiology, and health economics. The Journal is both international and multidisciplinary. It includes high quality practical papers, case studies and review papers.
期刊最新文献
A Commensurate Prior Model With Random Effects for Survival and Competing Risk Outcomes to Accommodate Historical Controls. Bayesian Sample Size Calculation in Small n, Sequential Multiple Assignment Randomized Trials (snSMART). Taylor Series Approximation for Accurate Generalized Confidence Intervals of Ratios of Log-Normal Standard Deviations for Meta-Analysis Using Means and Standard Deviations in Time Scale. A Bayesian Hybrid Design With Borrowing From Historical Study. WATCH: A Workflow to Assess Treatment Effect Heterogeneity in Drug Development for Clinical Trial Sponsors.
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