Combining Cox Regressions Across a Heterogeneous Distributed Research Network Facing Small and Zero Counts

no code implementations • 5 Jan 2021 • Martijn J. Schuemie, Yong Chen, David Madigan, Marc A. Suchard

Studies of the effects of medical interventions increasingly take place in distributed research settings using data from multiple clinical data sources including electronic health records and administrative claims.

Methodology

Stable discovery of interpretable subgroups via calibration in causal studies

1 code implementation • 23 Aug 2020 • Raaz Dwivedi, Yan Shuo Tan, Briton Park, Mian Wei, Kevin Horgan, David Madigan, Bin Yu

Building on Yu and Kumbier's PCS framework and for randomized experiments, we introduce a novel methodology for Stable Discovery of Interpretable Subgroups via Calibration (StaDISC), with large heterogeneous treatment effects.

Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model

2 code implementations • 5 Nov 2015 • Benjamin Letham, Cynthia Rudin, Tyler H. McCormick, David Madigan

We introduce a generative model called Bayesian Rule Lists that yields a posterior distribution over possible decision lists.