Email Address: firstname.lastname@example.org
Other Research Theme Collaborations: DART SURPH SOMX
BSU Research overviewMany diseases and medical conditions, such as cancers, dementia and rheumatic disorders, are multi-factorial and may exhibit a range of complex biological phenotypes reflecting the contribution of a multitude of genetic and environmental components. Even with rare or ultra orphan diseases, such as Gaucher, which may be the result of mutations in a single gene, varying symptomatology, outcomes and treatment responses can be displayed across patients with the disease. The complexity of diseases leads to many challenges ranging from the understanding of disease mechanisms and disease susceptibility to risk prediction and the development and application of treatments. Stratified medicine, where "homogeneous" groups of people likely to respond similarly to treatment or have similar underlying disease mechanism or outcome risk are sought based on molecular (biomarker) information, is a first step towards reducing biological variability and lies within the continuum of "patient therapy", in which empirical or "all comer" medicine sits at one end and precision medicine at the other. The fact that genetic, molecular and imaging modalities have transformed biology from an observational science to a more data and computationally intensive quantitative science has meant that the need for those who are trained in handling data, accounting for uncertainty and making inference is ever more paramount. Many of the issues and challenges confronting stratified medicine are of a statistical nature. We are undertaking a programme of research that encompasses risk stratification, prediction and validation, integrative and joint modelling of molecular and clinical data of various kinds and complexities (and their conversion into meaningful outputs that can inform health care decisions), mechanistic understanding and causality, treatment strategies and the design of innovative/purposeful clinical trials for biomarkers and dynamic treatment regimes. Brian Tom's personal page
Selected PapersO'Keeffe A G, Farewell D M, Tom B D M & Farewell V T (2016)Multiple imputation of missing composite outcomes in longitudinal data.
Statistics in Biosciences 8: 310-332
Yiu S, Tom B D M & Farewell V T (2016)Trivariate mover-stayer counting process models for investigating joint damage in psoriatic arthritis.
Statistics in Medicine 35: 5701-5716
Farewell V T, Long D L, Tom B D M, Yiu S L & Su L (2017)Two-part and related regression models for longitudinal data.
Annual Reviews of Statistics and its Applications 4: 283-315
Yiu S & Tom B D M (2017)A joint modelling approach for multi-state processes subject to resolution and under intermittent observations.
Statistics in Medicine 36: 496-508
Yiu S, Farewell V T & Tom B D M (2017)Exploring the existence of a stayer population with mover-stayer counting process models: Application to joint damage in psoriatic arthritis.
Journal of the Royal Statistical Society, Series C 66(4): 669-690
Kassanjee R, De Angelis D, Farah M, Hanson D, Labuschagne P, Laeyendecker O, Le Vu S, Tom B, Wang R & Welte A (2017)Cross-sectional HIV incidence surveillance: A benchmarking of approaches for estimating the 'mean duration of recent infection'.
Statistical Communications in Infectious Diseases 9(1): 20160002
Huang Z, Muniz-Terrera G & Tom B D M (2017)Power analysis to detect treatment effects in longitudinal clinical trials for Alzheimer's disease.
Alzheimer's & Dementia: Translational Research & Clinical Interventions 3(3): 360-366
Yiu S & Tom B D M (2017)Two-part models with stochastic processes for modelling longitudinal semicontinuous data:computationally efficient inference and modelling the overall marginal mean.
Statistical Methods in Medical Research : (Published Online)
Yiu S, Farewell V T & Tom B D M (2017)Clustered multi-state models with observation-level random effects, mover-stayer effects and dynamic covariates: modelling transition intensities and sojourn times in a study of psoriatic arthritis.
Journal of the Royal Statistical Society, Series C : (Published Online)