Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods is a research paper published in BMC Medical Research Methodology (2024). On theSindex it has a DataRank of 0. It has been cited 5 times.
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National Institutes of Health
Grant: NIA U19AG062682, UL1TR002389
National Institutes of Health
Grant: 5UL1TR002389-04
ITM 2.0: Advancing Translational Science in Metropolitan Chicago
National Institutes of Health
Grant: 5U19AG062682-04
ASPirin in Reducing Events in the Elderly - eXTension
FWCI
2.38
Citation Percentile
0.9%
Citation Trend
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MeSH Terms
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Sustainable Development Goals
Additional file 1 of Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods
Additional file 1 of Outcome risk model development for heterogeneity of treatment effect analyses: a comparison of non-parametric machine learning methods and semi-parametric statistical methods