24 citations · 33 across the 6 of their papers we have counts for
8 papers
Selective recruitment designs for improving observational studies using electronic health records
James E. Barrett, Aylin Cakiroglu, Catey Bunce +2
Large scale electronic health records (EHRs) present an opportunity to quickly identify suitable individuals in order to directly invite them to participate in an observational stu…
Replica analysis of overfitting in regression models for time-to-event data
ACC Coolen, JE Barrett, P Paga +1
Overfitting, which happens when the number of parameters in a model is too large compared to the number of data points available for determining these parameters, is a serious and…
Quantification of tumour evolution and heterogeneity via Bayesian epiallele detection
James E. Barrett, Andrew Feber, Javier Herrero +4
Motivation: Epigenetic heterogeneity within a tumour can play an important role in tumour evolution and the emergence of resistance to treatment. It is increasingly recognised that…
Information-adaptive clinical trials with selective recruitment and binary outcomes
James E. Barrett
Selective recruitment designs preferentially recruit individuals that are estimated to be statistically informative onto a clinical trial. Individuals that are expected to contribu…
Information-adaptive clinical trials: a selective recruitment design
James E. Barrett
We propose a novel adaptive design for clinical trials with time-to-event outcomes and covariates (which may consist of or include biomarkers). Our method is based on the expected…
Covariate dimension reduction for survival data via the Gaussian process latent variable model
James E. Barrett, Anthony C. C. Coolen
The analysis of high dimensional survival data is challenging, primarily due to the problem of overfitting which occurs when spurious relationships are inferred from data that subs…
Gaussian process regression for survival data with competing risks
James E. Barrett, Anthony C. C. Coolen
We apply Gaussian process (GP) regression, which provides a powerful non-parametric probabilistic method of relating inputs to outputs, to survival data consisting of time-to-event…
Dimensionality Detection and Integration of Multiple Data Sources via the GP-LVM
James Barrett, Anthony C. C. Coolen
The Gaussian Process Latent Variable Model (GP-LVM) is a non-linear probabilistic method of embedding a high dimensional dataset in terms low dimensional `latent' variables. In thi…