.png){kind=icon}
返回
Using Real-World Data to Extrapolate Evidence From Randomized Controlled Trials
.png){kind=icon}
OA
AI
DOI：10.1002/cpt.1210.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Randomized controlled trials (RCTs) provide evidence for regulatory agencies, shape clinical practice, influence formulary decisions, and have important implications for patients. However, many patient groups that are major consumers of drugs are under-represented in randomized trials. We review three methods to extrapolate evidence from trial participants to different target populations following market approval and discuss how these could be implemented in practice to support regulatory and health technology assessment decisions. Although these methods are not a substitute for less restrictive pre-approval RCTs or rigorous observational studies when sufficient data are available in the post-approval setting, they can help to fill the evidence gap that exists in the early marketing period. Early evidence using real-world data and methods for extrapolating evidence should be reported with clear explanation of assumptions and limitations especially when used to support regulatory and health technology assessment decisions.
Keyword:
CLINICAL-TRIALS
MEDICATIONS
ADJUSTMENT
EXCLUSION
MODELS
RISK
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
5.5
论文数:
8.6K
被引数:
1.9W
机构
引用论文
分享收藏Assessing the Generalizability of Randomized Trial Results to Target Populations.png)
PREVENTION SCIENCEOAAI
PREVENTION SCIENCEIF2.7
OAAI分享收藏Confounding Adjustment in Comparative Effectiveness Research Conducted Within Distributed Research NetworksMEDICAL CAREOAAI
MEDICAL CAREIF2.8
OAAI分享收藏Exclusion of Women of Childbearing Potential in Clinical Trials of Type 2 Diabetes Medications: A Review of Protocol-Based Barriers to EnrollmentDIABETES CAREOAAI
DIABETES CAREIF16.6
OAAI分享收藏
查看更多内容