Professor, Departments of Population Health and Child and Adolescent Psychiatry, New York University School of Medicine and Nathan S. Kline Institute for Psychiatric Research, USA
Hyung Park
Post-doctoral Fellow, Department of Population Health, New York University School of Medicine, USA
Adam Ciarleglio
Assistant Professor, Department of Biostatistics and Bioinformatics, Milken Institute School of Public Health, George Washington University, USA
R. Todd Ogden
Professor, Department of Biostatistics, Columbia University Mailman School of Public Health, USA
Thaddeus Tarpey
Professor, Department of Population Health, New York University School of Medicine, USA
This tutorial introduces recent developments in precision medicine for estimating treatment decision rules. The objective of these developments is to advance personalised healthcare by identifying an optimal treatment option for each individual patient based on each patient's characteristics. The methods detailed in this tutorial define composite variables from the patient measures that can be viewed as ‘biosignatures’ for differential treatment response, which we have termed ‘generated effect modifiers’. In contrast to most machine learning approaches to precision medicine, these biosignatures are derived from linear and non-linear regression models and thus have the advantage of easy visualisation and ready interpretation. The methods are illustrated using examples from randomised clinical trials.
