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Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments
Konstantina Chalkou1,2,3, Tasnim Hamza1,2, Pascal Benkert4
1Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
Research Synthesis Methods
|March 19, 2024
Summary
This study presents an advanced network meta-regression model that integrates diverse data sources, including individual participant data (IPD) and aggregate data (AD), to predict personalized treatment effects for better patient outcomes.
Area of Science:
- Biostatistics
- Clinical Epidemiology
- Health Outcomes Research
Background:
- Treatment effectiveness varies significantly among patients.
- Previous models synthesized randomized trials to assess treatment effect heterogeneity.
- Existing models had limitations in integrating diverse data types and sources.
Purpose of the Study:
- To extend a two-stage network meta-regression prediction model.
- To incorporate aggregate data (AD) and individual participant data (IPD).
- To combine evidence from randomized and non-randomized studies for enhanced treatment effect estimation.
Main Methods:
- Developed a three-stage approach integrating prognostic and network meta-regression models.
- Stage 1: Prognostic model for baseline risk prediction using cohort data.
- Stage 2: Recalibration of the prognostic model for randomized trial participants.
- Stage 3: Network meta-regression incorporating baseline risk as an effect modifier, combining AD and IPD from randomized clinical trials.
Main Results:
- Patient characteristics influence baseline risk, which modifies drug effects.
- The model successfully integrated heterogeneous data sources (AD, IPD, randomized, non-randomized).
- Demonstrated personalized prediction of health outcomes for multiple sclerosis treatments.
Conclusions:
- The enhanced model provides personalized health outcome predictions.
- It effectively synthesizes diverse evidence, including non-randomized data.
- This approach improves the estimation of heterogeneous treatment effects for precision medicine.
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