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Publications

Modelling the impact of rapid tests, tracing and distancing in lower-income countries suggest optimal policies varies with rural-urban settings

Jiang X. et al, (2021)

Epidemiological changes on the Isle of Wight after the launch of the NHS Test and Trace programme: a preliminary analysis.

Kendall M. et al, (2020), The Lancet. Digital health, 2, e658 - e666

Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error

Watson JA. and Holmes CC., (2020), Trials, 21

Correction to: Graphing and reporting heterogeneous treatment effects through reference classes.

Watson JA. and Holmes CC., (2020), Trials, 21

A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices

Watson JA. et al, (2020), PLOS Genetics, 16, e1009037 - e1009037

A FRAMEWORK FOR ADAPTIVE MCMC TARGETING MULTIMODAL DISTRIBUTIONS

Pompe E. et al, (2020), ANNALS OF STATISTICS, 48, 2930 - 2952

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera S. et al, (2020), The Lancet Digital Health, 2, e549 - e560

Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension

Liu X. et al, (2020), The Lancet Digital Health, 2, e537 - e548

Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension

Cruz Rivera S. et al, (2020), Nature Medicine, 26, 1351 - 1363

NRF2 metagene signature is a novel prognostic biomarker in colorectal cancer.

O'Cathail SM. et al, (2020), Cancer genetics, 248-249, 1 - 10

Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis.

Benedetto U. et al, (2020), The Journal of thoracic and cardiovascular surgery

Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study.

Banerjee A. et al, (2020), Lancet (London, England), 395, 1715 - 1725

Graphing and reporting heterogeneous treatment effects through reference classes.

Watson JA. and Holmes CC., (2020), Trials, 21

Testing for dependence on tree structures

Behr M. et al, (2020), Proceedings of the National Academy of Sciences, 117, 9787 - 9792

Machine learning and artificial intelligence research for patient benefit: 20 critical questions on transparency, replicability, ethics, and effectiveness.

Vollmer S. et al, (2020), BMJ (Clinical research ed.), 368

Cost-Effectiveness Analysis of Baseline Testing for Resistance-Associated Polymorphisms to Optimize Treatment Outcome in Genotype 1 Noncirrhotic Treatment-Naïve Patients With Chronic Hepatitis C Virus.

Fawsitt CG. et al, (2020), Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research, 23, 180 - 190

On the marginal likelihood and crossvalidation

Fong E. and Holmes CC., (2020), Biometrika

Improving the quality of machine learning in health applications and clinical research

Mateen BA. et al, (2020), Nature Machine Intelligence

Author Correction: Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed.

CONSORT-AI and SPIRIT-AI Steering Group None., (2019), Nature medicine, 25

Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed.

CONSORT-AI and SPIRIT-AI Steering Group None., (2019), Nature medicine, 25, 1467 - 1468

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