Multi-indication evidence synthesis in oncology health technology assessment
arxiv(2023)
摘要
Background: Cancer drugs receive licensing extensions to include additional
indications as trial evidence on treatment effectiveness accumulates. We
investigate how sharing information across indications can strengthen the
inferences supporting Health Technology Assessment (HTA). Methods: We applied
meta-analytic methods to randomised trial data on bevacizumab to share
information across cancer indications on the treatment effect on overall
survival (OS) or progression-free survival (PFS), and on the surrogate
relationship between effects on PFS and OS. Common or random parameters were
used to facilitate sharing and the further flexibility of mixture models was
explored. Results: OS treatment effects lacked precision when pooling data
available at present-day within each indication, particularly for indications
with few trials. There was no suggestion of heterogeneity across indications.
Sharing information across indications provided more precise inferences on
treatment effects, and on surrogacy parameters, with the strength of sharing
depending on the model. When a surrogate relationship was used to predict OS
effects, uncertainty was only reduced with sharing imposed on PFS effects in
addition to surrogacy parameters. Corresponding analyses using the earlier,
sparser evidence available for particular HTAs showed that sharing on both
surrogacy and PFS effects did not notably reduce uncertainty in OS predictions.
Limited heterogeneity across indications meant that the added flexibility of
mixture models was unnecessary. Conclusions: Meta-analysis methods can be
usefully applied to share information on treatment effectiveness across
indications to increase the precision of target indication estimates in HTA.
Sharing on surrogate relationships requires caution, as meaningful precision
gains require larger bodies of evidence and clear support for surrogacy from
other indications.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要