A comparison of approaches for combining predictive markers for personalised treatment recommendations

In the presence of heterogeneous treatment effects, it is desirable to divide patients into subgroups based on their expected response to treatment. This is formalised via a personalised treatment recommendation: an algorithm that uses biomarker measurements to select treatments. It could be that multiple, rather than single, biomarkers better predict these subgroups. However, finding the optimal combination of multiple biomarkers can be a difficult prediction problem.

We described three parametric methods for finding the optimal combination of biomarkers in a personalised treatment recommendation, using randomised trial data: a regression approach that models outcome using treatment by biomarker interactions; an approach proposed by Kraemer that forms a combined measure from individual biomarker weights, calculated on all treated and control pairs; and a novel modification of Kraemer's approach that utilises a prognostic score to sample matched treated and control subjects. Using Monte Carlo simulations under multiple data-generating models, we compare these approaches and draw conclusions based on a measure of improvement under a personalised treatment recommendation compared to a standard treatment. The three methods are applied to data from a randomised trial of home-delivered pragmatic rehabilitation versus treatment as usual for patients with chronic fatigue syndrome (the FINE trial). Prior analysis of this data indicated some treatment effect heterogeneity from multiple, correlated biomarkers.

The regression approach outperformed Kraemer's approach across all data-generating scenarios. The modification of Kraemer's approach leads to improved treatment recommendations, except in the case where there was a strong unobserved prognostic biomarker. In the FINE example, the regression method indicated a weak improvement under its personalised treatment recommendation algorithm.

The method proposed by Kraemer does not perform better than a regression approach for combining multiple biomarkers. All methods are sensitive to misspecification of the parametric models.