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

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.

SUBMITTER: Petkova E

PROVIDER: S-EPMC7001471 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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

Petkova Eva E Park Hyung H Ciarleglio Adam A Todd Ogden R R Tarpey Thaddeus T

BJPsych open 20191203 1

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 m ...[more]

PMID: 31791433

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

Publications

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

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