Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments

TL;DR

This paper presents an extended model that combines diverse data sources, including randomized and non-randomized studies, to predict individualized treatment effects in multiple sclerosis, enhancing personalized medicine.

Contribution

The novel approach integrates aggregate and individual data from various study types into a unified model for estimating heterogeneous treatment effects.

Findings

The model accurately predicts relapse risk based on patient characteristics.

It demonstrates the ability to personalize treatment effect estimates.

The approach effectively synthesizes diverse evidence sources.

Abstract

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD RCTs to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.