Work

Optimising treatment decision rules through generated effect modifiers: a precision medicine tutorial

Year: 2019

Type: article

Abstract: 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.

Source: BJPsych Open

DOI: 10.1192/bjo.2019.85

Authors: Eva Petkova, Hyung Park, Adam Ciarleglio, R. Todd Ogden, Thaddeus Tarpey

Institutions: Nathan Kline Institute for Psychiatric Research, New York University, George Washington University, Columbia University

Language: English

Topic: Advanced Causal Inference Techniques

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $1,600