Stable Discovery of Interpretable Subgroups via Calibration in Causal Studies
Abstract
Building on Yu and Kumbier's predictability, computability and stability (PCS) framework and for randomised experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC), with large heterogeneous treatment effects. StaDISC was developed during our re‐analysis of the 1999–2000 VIGOR study, an 8076‐patient randomised controlled trial that compared the risk of adverse events from a then newly approved drug, rofecoxib (Vioxx), with that from an older drug naproxen. Vioxx was found to, on average and in comparison with naproxen, reduce the risk of gastrointestinal events but increase the risk of thrombotic cardiovascular events. Applying StaDISC, we fit 18 popular conditional average treatment effect (CATE) estimators for both outcomes and use calibration to demonstrate their poor global performance. However, they are locally well‐calibrated and stable, enabling the identification of patient groups with larger than (estimated) average treatment effects. In fact, StaDISC discovers three clinically interpretable subgroups each for the gastrointestinal outcome (totalling 29.4% of the study size) and the thrombotic cardiovascular outcome (totalling 11.0%). Complementary analyses of the found subgroups using the 2001–2004 APPROVe study, a separate independently conducted randomised controlled trial with 2587 patients, provide further supporting evidence for the promise of StaDISC.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 01, 2020
- Source ID
- 10.1111/insr.12427
Entities
People
- Bin Yu
- Briton Park
- David Madigan
- Kevin Horgan
- Mian Wei
- Raaz Dwivedi
- Yan Shuo Tan
Organizations
- Amazon
- Army Research Office
- Chan Zuckerberg Initiative
- National Science Foundation
- Northeastern University
- Office of Naval Research
- Statistics New Zealand