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Estimating individualized treatment effects using individual participant data meta-analysis

Abstract : Different approaches can be used to estimate individualized treatment effects with Individual Participant Data Meta-Analyses (IPD-MA). We compared four one-stage models: random effects (RE), stratified intercept (SI), rank-1 (R1) and fully stratified (FS) models, built with two different strategies constructed with a Monte Carlo simulation study in which we explored different scenarios with a binary or a time-to-event outcome. To evaluate the performance of the models, we used the c-statistic for benefit, the calibration of predictions and the mean squared error. The different models were also used on the INDANA IPD-MA, comparing an anti-hypertensive treatment to no treatment or placebo (N = 40 237, 836 events). Simulation results showed that the random effects and the stratified intercept models performed well for both binary and time-to-event outcomes. For the INDANA dataset with a binary outcome, the random effects model had the best performance.
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Contributor : Raphaël Porcher Connect in order to contact the contributor
Submitted on : Thursday, July 21, 2022 - 2:51:05 PM
Last modification on : Friday, August 5, 2022 - 2:54:01 PM


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  • HAL Id : hal-03735613, version 1


Florie Brion Bouvier, Anna Chaimani, François Gueyffier, Guillaume Grenet, Raphaël Porcher. Estimating individualized treatment effects using individual participant data meta-analysis. 2022. ⟨hal-03735613⟩



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