Skip to content
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Menu
Home
About Us
Resources
Profiles Metrics
Authors Directory
Institutions Directory
Top Authors
Top Institutions
Top Sponsors
AI Digest
Contact Us
Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
mathematics
A two-stage prediction model for heterogeneous effects of treatments
Statistics in Medicine, Volume 40, No. 20, Year 2021
Notification
URL copied to clipboard!
Description
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. © 2021 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.
Authors & Co-Authors
Steyerberg, Ewout Willem
Netherlands, Leiden
Leids Universitair Medisch Centrum
Egger, Matthias
Switzerland, Bern
University of Bern
United Kingdom, Bristol
University of Bristol
Pellegrini, Fabio
Switzerland, Zug
Biogen Switzerland ag
Salanti, Georgia
Switzerland, Bern
University of Bern
Statistics
Citations: 14
Authors: 4
Affiliations: 5
Identifiers
Doi:
10.1002/sim.9034
ISSN:
02776715
Research Areas
Disability
Health System And Policy
Study Design
Randomised Control Trial
Study Approach
Systematic review