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日期 2025 年 5 月 29 日, 星期四

时间 10am Japan/Korea, 9am in China

解决方案:Early Clinical, Late Clinical

产品: CODEX

概述

Model-based meta-analysis (MBMA) is a valuable tool that leverages published clinical outcome data to address critical drug development questions. Applications of MBMA can range from, for example, understanding the competitive landscape and the likelihood of a new drug’s success to benchmarking a new drug’s performance with a synthetic control arm to leveraging covariate relationships from competitors to design better trials. With access to relevant, publicly-available clinical study via the CODEX Clinical Outcome Databases, you can quickly understand what safety and efficacy targets are necessary for differentiation, optimize trial design, and improve the accuracy of early-phase and go/no-go decisions.

In this educational webinar, our experts will address common questions such as; When and how can CODEX and MBMA be integrated into your workflow? Is MBMA useful in the early stages of development in addition to later stages? What if your team lacks MBMA specialists? How to use early endpoints/biomarkers to predict late endpoints?

We’ll explore MBMA’s applications across all phases of drug development, showing how you can utilize this tool to its fullest potential at any stage.

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您将学到什么?

  • Fundamentals of MBMA – How to apply MBMA from early to late stages of drug development
  • Key applications of MBMA across various phases
  • Available data types in the CODEX Clinical Outcome Database
  • How to embrace published clinical outcome data of CODEX for decision making

本次网络研讨会对以下人员最有帮助:

  • Clinical pharmacologists
  • 生物统计学家
  • Pharmacometricians
  • Professionals involved in clinical development, strategy, and trial design
  • Anyone new to MBMA, keen to learn basics of MBMA
  • Anyone who considers using published data for decision making in early stage such as competitive landscape analysis, efficacy of similar drugs, etc.

演讲嘉宾:

Matthew Zierhut
Matthew Zierhut, PhD MBA

Vice President, Quantitative Science Services, Certara

Matt advances the integration of published clinical outcomes data into development decisions and commercial and regulatory strategy via model-based meta-analysis (MBMA). Matt 与临床开发团队密切合作,确保在做出最关键决策的时候,能利用 MBMA 发挥最佳影响力。

Shuai Fu, PhD

Associate Principal Scientist, Quantitative Science Services, Certara

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