42 citations · 145 across the 28 of their papers we have counts for
8 papers · 1 filter
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…
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…
Statistical methods for Mendelian models with multiple genes and cancers
Jane W. Liang, Gregory E. Idos, Christine Hong +3
Risk evaluation to identify individuals who are at greater risk of cancer as a result of heritable pathogenic variants is a valuable component of individualized clinical management…
Bayesian Combinatorial Multi-Study Factor Analysis
Isabella N. Grabski, Roberta De Vito, Lorenzo Trippa +1
Analyzing multiple studies allows leveraging data from a range of sources and populations, but until recently, there have been limited methodologies to approach the joint unsupervi…