Cargando…
Graphing and reporting heterogeneous treatment effects through reference classes
BACKGROUND: Exploration and modelling of heterogeneous treatment effects as a function of baseline covariates is an important aspect of precision medicine in randomised controlled trials (RCTs). Randomisation generally guarantees the internal validity of an RCT, but heterogeneity in treatment effect...
Autores principales: | Watson, James A., Holmes, Chris C. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7204233/ https://www.ncbi.nlm.nih.gov/pubmed/32381030 http://dx.doi.org/10.1186/s13063-020-04306-1 |
Ejemplares similares
-
Correction to: Graphing and reporting heterogeneous treatment effects through reference classes
por: Watson, James A., et al.
Publicado: (2020) -
Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error
por: Watson, James A., et al.
Publicado: (2020) -
Predicting combinations of drugs by exploiting graph embedding of heterogeneous networks
por: Song, Fei, et al.
Publicado: (2022) -
Assessing and reporting heterogeneity in treatment effects in clinical trials: a proposal
por: Kent, David M, et al.
Publicado: (2010) -
GraphDTI: A robust deep learning predictor of drug-target interactions from multiple heterogeneous data
por: Liu, Guannan, et al.
Publicado: (2021)