Chris Holmes
Professors of Biostatistics in Genomics
I have a broad interest in the theory, methods and applications of statistics and statistical modelling. My background and beliefs lie in Bayesian statistics which provides a unified framework to stochastic modelling and information processing. I am particularly interested in pattern recognition and nonlinear, nonparametric methods.
Recent publications
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Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error
Journal article
Watson JA. and Holmes CC., (2020), Trials, 21
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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Journal article
Cruz Rivera S. et al, (2020), Nature Medicine, 26, 1351 - 1363
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NRF2 metagene signature is a novel prognostic biomarker in colorectal cancer.
Journal article
O'Cathail SM. et al, (2020), Cancer genetics, 248-249, 1 - 10
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Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.
Journal article
Benedetto U. et al, (2020), The Journal of thoracic and cardiovascular surgery
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Graphing and reporting heterogeneous treatment effects through reference classes.
Journal article
Watson JA. and Holmes CC., (2020), Trials, 21