42 citations · 145 across the 28 of their papers we have counts for
30 papers
Multi-Study R-Learner for Estimating Heterogeneous Treatment Effects Across Studies Using Statistical Machine Learning
Cathy Shyr, Boyu Ren, Prasad Patil +1
Estimating heterogeneous treatment effects (HTEs) is crucial for precision medicine. While multiple studies can improve the generalizability of results, leveraging them for estimat…
Defining Replicability of Prediction Rules
Giovanni Parmigiani
In this article I propose an approach for defining replicability for prediction rules. Motivated by a recent NAS report, I start from the perspective that replicability is obtainin…
Bayesian Meta-Analysis of Penetrance for Cancer Risk
Thanthirige Lakshika M. Ruberu, Danielle Braun, Giovanni Parmigiani +1
Multi-gene panel testing allows many cancer susceptibility genes to be tested quickly at a lower cost making such testing accessible to a broader population. Thus, more patients ca…
Multi-Study Boosting: Theoretical Considerations for Merging vs. Ensembling
Cathy Shyr, Pragya Sur, Giovanni Parmigiani +1
Cross-study replicability is a powerful model evaluation criterion that emphasizes generalizability of predictions. When training cross-study replicable prediction models, it is cr…
Cross-Study Replicability in Cluster Analysis
Lorenzo Masoero, Emma Thomas, Giovanni Parmigiani +2
In cancer research, clustering techniques are widely used for exploratory analyses and dimensionality reduction, playing a critical role in the identification of novel cancer subty…
Optimal Ensemble Construction for Multi-Study Prediction with Applications to COVID-19 Excess Mortality Estimation
Gabriel Loewinger, Rolando Acosta Nunez, Rahul Mazumder +1
It is increasingly common to encounter prediction tasks in the biomedical sciences for which multiple datasets are available for model training. Common approaches such as pooling d…