ArticleBioinformatics advances2022
dipm: an R package implementing the Depth Importance in Precision Medicine (DIPM) tree and Forest-based method.
Article in Bioinformatics advances, 2022. 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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3 authors.
Funding
Abstract
Summary: The Depth Importance in Precision Medicine (DIPM) method is a classification tree designed for the identification of subgroups relevant to the precision medicine setting. In this setting, a relevant subgroup is a subgroup in which subjects perform either especially well or poorly with a particular treatment assignment. Herein, we introduce, dipm, a novel R package that implements the DIPM method using R code that calls a program in C. Availability and implementation: dipm is available under a GPL-3 licence on CRAN https://cran.r-project.org/web/packages/dipm/index.html and at https://ysph.yale.edu/c2s2/software/dipm. It is continuously being developed at https://github.com/chenvict/dipm. Supplementary information: Supplementary data are available at
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.