Abstract

Treatment effects vary across different patients, and estimation of this variability is essential for clinical decision‐making. We aimed to develop a model estimating the benefit of alternative treatment options for individual patients, extending a risk modeling approach in a network meta‐analysis framework. We propose a two‐stage prediction model for heterogeneous treatment effects by combining prognosis research and network meta‐analysis methods where individual patient data are available. In the first stage, a prognostic model to predict the baseline risk of the outcome. In the second stage, we use the 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 relapses 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 the baseline risk of relapse, which in turn moderates the benefit expected for each of the treatments. For high‐risk patients, the treatment that minimizes the risk of relapse in 2 years is Natalizumab, whereas Dimethyl Fumarate might be a better option for low‐risk patients. Our approach can be easily extended to all outcomes of interest and has the potential to inform a personalized treatment approach.

Highlights

  • Personalized predictions are important for clinical decision-making

  • We developed a prediction model for heterogeneous treatment effects that combines risk modelling and network meta-analytical methods to make personalized predictions for an outcome of interest and to inform treatment decisions

  • We extended the idea of risk modelling approach (1) by combining network meta-analysis methods that allow comparing many treatment options via direct and indirect evidence (35)

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Summary

Introduction

Personalized predictions are important for clinical decision-making. The question ‘Which treatment is best?’ can have two very different meanings: ‘Which treatment is best on average?’ or ‘Which treatment is best for a specific patient?’ Patients often experience different outcomes under the same treatment. One patient may benefit more by a treatment from which another patient may benefit less. It is essential to identify via risk modelling approach those patient characteristics that influence treatment effects in order to choose the best option for a given patient effects. Prediction models aim to identify and estimate the impact of patient, intervention and setting characteristics on future health outcomes. The baseline risk of patients is often a determinant of heterogeneous treatment effects (1)

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