Science

We develop and apply efficient implementations of advanced Bayesian statistical tools and modelling approaches to analyse epidemiological and medical trial data sets, with emphasis on high-dimensional ones. Clinical outcomes can take the form of event times, treatment response, or disease severity. Our methods can detect and map latent association or base hazard rate heterogeneity, correct survival analysis regression outcomes for overfitting, and optimise covariate selections. This enables objective data-driven stratification and reproducible outcome prediction, and leads to optimized individual risk scores and treatment response scores.

Our research is carried out and applied to medical data in partnerships with teams in several UK and international universities and medical centres, mainly within the EU, Japan, and the USA. While our focus is on fundamental methodological innovation, our research themes, summarised below, are strictly driven by the needs of modern personalised (or 'precision') medicine.

Selected publications:

  • Home Replica analysis of Bayesian data clustering.

    Mozeika and ACC Coolen. 2019. Journal of Physics A.

  • Analysis of overfitting in the regularized Cox model.

    M Sheikh and ACC Coolen. 2019. Journal of Physics A.

  • Accurate Bayesian classification without hyperparameter cross-validation.

    M Sheikh and ACC Coolen. 2019. Journal of Classification.

  • Replica analysis of overfitting in regression models for time-to-event data.

    ACC Coolen, J Barrett, P Paga and C Perez Vicente. 2017. J Phys A.

  • Generating random networks and graphs.

    ACC Coolen, A Annibale and ES Roberts. 2017. Oxford University Press.

  • A latent class model for competing risks.

    M Rowley et al. 2017. Statistics in Medicine.

  • Bayesian clinical classification from high-dimensional data: Signatures versus variability.

    A Shalabi et al. 2016. Stat Methods Med Res.

  • Covariate dimension reduction for survival data via the Gaussian process latent variable model.

    JE Barrett and ACC Coolen. 2015. Statistics in Medicine.​​

    • Research Activities:

    Bayesian latent class analysis of complex epidemiological data describing heterogeneous cohorts, with either time-to-event or ordinal class outcome variables

    Bayesian latent class analysis of complex epidemiological data with informative censoring and competing risks

    Bayesian data-driven retrospective patient stratification for clinical trial data with inhomogeneous treatment associations​

    Generation of accurate personalised risk scores and treatment response scores

    Overfitting corrections for regression parameters in survival analysis

    Cross-validation based optimisation of covariates in Bayesian survival analysis, including covariate multiplexing

    Bayesian discriminative analysis for very high dimensional data, based on full analytical evaluation of parameter integrals

    Nonlinear dimension reduction and multivariate survival analysis for very high dimensional data​​

    Generation of rigorously unbiased null models for proteomic or transcriptomic signalling networks, with tailored topological characteristics

    Bayesian methods for medical image analysis

    Traditional and adaptive medical trial design

    Tumour infiltration biomarkers and immunotherapy modelling