6 citations · 11 across the 8 of their papers we have counts for
12 papers
Data-driven controlled subgroup selection in clinical trials
Manuel M. Müller, Björn Bornkamp, Frank Bretz +7
Subgroup selection in clinical trials is essential for identifying patient groups that react differently to a treatment, thereby enabling personalised medicine. In particular, subg…
AMUSE: Adaptive Model Updating using a Simulated Environment
Louis Chislett, Catalina A. Vallejos, Timothy I. Cannings +1
Prediction models frequently face the challenge of concept drift, in which the underlying data distribution changes over time, weakening performance. Examples can include models wh…
Random-projection ensemble dimension reduction
Wenxing Zhou, Timothy I. Cannings
We introduce a new framework for dimension reduction in the context of high-dimensional regression. Our proposal is to aggregate an ensemble of random projections, which have been…
Isotonic subgroup selection
Manuel M. Müller, Henry W. J. Reeve, Timothy I. Cannings +1
Given a sample of covariate-response pairs, we consider the subgroup selection problem of identifying a subset of the covariate domain where the regression function exceeds a pre-d…
Nonparametric classification with missing data
Torben Sell, Thomas B. Berrett, Timothy I. Cannings
We introduce a new nonparametric framework for classification problems in the presence of missing data. The key aspect of our framework is that the regression function decomposes i…
Optimal subgroup selection
Henry W. J. Reeve, Timothy I. Cannings, Richard J. Samworth
In clinical trials and other applications, we often see regions of the feature space that appear to exhibit interesting behaviour, but it is unclear whether these observed phenomen…
Adaptive transfer learning
Henry W. J. Reeve, Timothy I. Cannings, Richard J. Samworth
In transfer learning, we wish to make inference about a target population when we have access to data both from the distribution itself, and from a different but related source dis…
Data-driven design of targeted gene panels for estimating immunotherapy biomarkers
Jacob R. Bradley, Timothy I. Cannings
We introduce a novel data-driven framework for the design of targeted gene panels for estimating exome-wide biomarkers in cancer immunotherapy. Our first goal is to develop a gener…
Random projections: data perturbation for classification problems
Timothy I. Cannings
Random projections offer an appealing and flexible approach to a wide range of large-scale statistical problems. They are particularly useful in high-dimensional settings, where we…
The correlation-assisted missing data estimator
Timothy I. Cannings, Yingying Fan
We introduce a novel approach to estimation problems in settings with missing data. Our proposal -- the Correlation-Assisted Missing data (CAM) estimator -- works by exploiting the…
A Framework for Implementing Machine Learning on Omics Data
Geoffroy Dubourg-Felonneau, Timothy Cannings, Fergal Cotter +4
The potential benefits of applying machine learning methods to -omics data are becoming increasingly apparent, especially in clinical settings. However, the unique characteristics…
Local nearest neighbour classification with applications to semi-supervised learning
Timothy I. Cannings, Thomas B. Berrett, Richard J. Samworth
We derive a new asymptotic expansion for the global excess risk of a local--nearest neighbour classifier, where the choice of may depend upon the test point. This expansion…