Structured Assessment of Treatment Effect Heterogeneity

Bjoern Bornkamp Co-Author
TU Dortmund
Kostas Sechidis Co-Author
Novartis Pharmaceuticals
Yao Chen Co-Author
Purdue University
Cong Zhang Co-Author
Jiarui Lu Co-Author
Novartis Pharmaceuticals Corporation
Jelena Cuklina Co-Author
Novartis Pharmaceuticals
Ardalan Mirshani Co-Author
David Ohlssen Co-Author
Novartis Pharmaceuticals
Marc Vandemeulebroecke Co-Author
Novartis Pharma AG
Hui Sun Speaker
Novartis Pharmaceuticals
Tuesday, Aug 8: 9:25 AM - 9:50 AM
Invited Paper Session 
Metro Toronto Convention Centre 
The heterogeneity of treatment effects is challenging and may not be replicated in new trials. There is a simple reason for this: Clinical trials are powered to provide reliable inference on the overall treatment effect. Estimates of subgroup treatment effects will be variable and unreliable in a single trial. As data alone cannot provide definitive answers on subgroup effects, it is important that a workflow around assessing treatment effect heterogeneity should include clinical considerations. A structured approach should be followed to identify variables that modulate the treatment effect and predicting possibly heterogeneous treatment effects on new patients. The workflow includes methodological learnings from the 2022 Analytics Subgroup Challenge at Novartis.