Science

We develop and apply advanced Bayesian statistical inference tools and modelling approaches to analyse epidemiological and medical trial data sets, with emphasis on high-dimensionality and latent heterogeneity. 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 more 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 international universities and medical centres. While our focus is on methodological innovation, our research themes are strictly driven by the needs of modern personalised (or 'precision') medicine.

Selected recent publications:

  • Acute immune signatures and their legacies in severe acute respiratory syndrome coronavirus-2 infected cancer patients.

    S Abdul-Jawad et al. 2021. Cancer Cell.

  • Genetic risk, lifestyle, and age-related macular degeneration in Europe: the EYE-RISK consortium.

    JM Colijn et al. 2021. Ophthalmology.

  • HER2-HER3 heterodimer quantification by FRET-FLIM and patient subclass analysis of the COIN colorectal trial.

    PR Barber et al. 2020. Journal of the National Cancer Institute.

  • Replica analysis of overfitting in generalized linear regression models.

    ACC Coolen et al. 2019. Journal of Physics A.

  • Accurate Bayesian data classification without hyperparameter cross-validation.

    M Sheikh and ACC Coolen. 2020. Journal of Classification.

  • Analysis of overfitting in the regularized Cox model.

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

  • Heterogeneity in risk of prostate cancer: a Swedish population-based cohort study of competing risks and type-2 diabetes mellitus.

    C Haggstrom et al. 2018. International Journal of Cancer

    • 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