ArticleStatistical methods in medical research2020
Integrating subgroups with mixed-type endpoints in early phase oncology trials.
Article in Statistical methods in medical research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Testing anti-cancer agents with multiple disease subtypes is challenging and it becomes more complicated when the subgroups have different types of endpoints (such as binary endpoints of tumor response and progression-free survival endpoints). When this occurs, one common approach in oncology is to conduct a series of small screening trials in specific patient subgroups, and these trials are typically run in parallel, independent of each other. However, this approach does not consider the possibility that some of the patient subpopulations respond similarly to therapy. In this article, we developed a simple approach to jointly model subgroups with mixed-type endpoints, which allows borrowing strength across subgroups for efficient estimation of treatment effects.
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