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A machine learning method for subgroup analysis of randomized controlled trials

View ORCID ProfileLjubomir Buturović
doi: https://doi.org/10.1101/338996
Ljubomir Buturović
Clinical Persona Inc., East Palo Alto, CA, United States
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Abstract

We developed a machine learning method for subgroup analyses of randomized controlled trials (RCT), and applied it to the results of the SPRINT RCT for treatment of hypertension. To date, the subgroup analyses mostly focused on detecting associations between certain factors and outcome, in the hope that the results will point out biologically (for example, carriers of a certain mutation) or clinically (for example, smokers) distinct subgroups with different outcomes. This seldom worked in the sense of re-launching the intervention for the detected subgroup only and successfully treating it. In contrast, we propose an empirical and general method to develop a predictive multivariate classifier using the RCT outcomes and baseline data. The classifier identifies patients likely to benefit from the intervention, is not limited to a single factor of interest, and is ready for validation in a subsequent pivotal trial. We believe this approach has a better chance of succeeding in identifying the relevant subgroups because of increased accuracy made possible by the use of multiple predictor variables, and opportunity to use advanced machine learning. The method effectiveness is demonstrated by the analysis of the SPRINT trial.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted June 04, 2018.
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A machine learning method for subgroup analysis of randomized controlled trials
Ljubomir Buturović
bioRxiv 338996; doi: https://doi.org/10.1101/338996
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A machine learning method for subgroup analysis of randomized controlled trials
Ljubomir Buturović
bioRxiv 338996; doi: https://doi.org/10.1101/338996

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