  Treatment effects vary across different patients and estimation of this
variability is important for clinical decisions. The aim is to develop a model
to estimate the benefit of alternative treatment options for individual
patients. Hence, we developed a two-stage prediction model for heterogeneous
treatment effects, by combining prognosis research and network meta-analysis
methods when individual patient data is available. In a first stage, we develop
a prognostic model and we predict the baseline risk of the outcome. In the
second stage, we use this baseline risk score from the first stage as a single
prognostic factor and effect modifier in a network meta-regression model. We
apply the approach to a network meta-analysis of three randomized clinical
trials comparing the relapse rate in Natalizumab, Glatiramer Acetate and
Dimethyl Fumarate including 3590 patients diagnosed with relapsing-remitting
multiple sclerosis. We find that the baseline risk score modifies the relative
and absolute treatment effects. Several patient characteristics such as age and
disability status impact on the baseline risk of relapse, and this in turn
moderates the benefit that may be expected for each of the treatments. For
high-risk patients, the treatment that minimizes the risk to relapse in two
years is Natalizumab, whereas for low-risk patients Dimethyl Fumarate Fumarate
might be a better option. Our approach can be easily extended to all outcomes
of interest and has the potential to inform a personalised treatment approach.
