Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments

by   Konstantina Chalkou, et al.

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD RCTs to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.


page 27

page 29

page 37

page 39

page 40


A two-stage prediction model for heterogeneous effects of many treatment options: application to drugs for Multiple Sclerosis

Treatment effects vary across different patients and estimation of this ...

Synthesizing cross-design evidence and cross-format data using network meta-regression

In network meta-analysis (NMA), we synthesize all relevant evidence abou...

Efficient Integration of Aggregate Data and Individual Patient Data in One-Way Mixed Models

Often both Aggregate Data (AD) studies and Individual Patient Data (IPD)...

Augmented Learning of Heterogeneous Treatment Effects via Gradient Boosting Trees

Heterogeneous treatment effects (HTE) based on patients' genetic or clin...

Stable discovery of interpretable subgroups via calibration in causal studies

Building on Yu and Kumbier's PCS framework and for randomized experiment...

Decision making in cancer: Causal questions require causal answers

Treatment decisions in cancer care are guided by treatment effect estima...

Personalized Prediction of Future Lesion Activity and Treatment Effect in Multiple Sclerosis from Baseline MRI

Precision medicine for chronic diseases such as multiple sclerosis (MS) ...

Please sign up or login with your details

Forgot password? Click here to reset