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.

Keywords: Treatment effect modifiers; personalised treatment assignment; single index models; treatment decision rule (TDR); value of TDR.

Fig. 1

The relationship between the derived generated effect modifier (GEM) and reading achievement outcome for ParentCorps (light green) and pre-kindergarten as usual (dark green) interventions.

Fig. 2

The relationship between the derived single index z = α'x and change in depression severity for placebo (dark green curve) and the drug (light green curve) treatment.

Fig. 3

Values of the treatment decision rules based on the non-linear (single-index model with multiple-links (SIMML)) and linear generated effect modifier approaches, and the two trivial treatment decisions to treat everyone with the antidepressant (Drug all) or with placebo (Placebo all) with 95% confidence intervals.