Hannah Ensor

Hannah's picture

Statistician

Biomathematics and Statistics Scotland
JCMB, The King's Buildings,
Peter Guthrie Tait Road,
EDINBURGH, EH9 3FD, Scotland, UK.

Tel: +44 (0)131 651 7288

Hannah Ensor

Research Interests

Joint generalised mixed models, analysis of ordinal outcomes, evaluating surrogate outcomes, information theory, counterfactuals and causality.

Background

My background is mostly in human health and clinical trials methodology. I have various experience of insuring statistical rigour in trial analysis and experimental design.

I have: an undergraduate degree from the University of Glasgow in Mathematics and Statistics; a Masters in Biometry from the University of Reading; and undertook an MRC Trials Methodology Hub PhD on “The evaluation of surrogate outcomes: methodological extensions to ordinal outcomes with applications in acute stroke” at the University of Edinburgh. I also previously worked as the sole statistician for the Leukaemia Research Cytogenetics Group at the Northern Institute of Cancer Research at Newcastle University. This group holds all the cytogenetic, biological and clinical trials information on leukaemia patients in the UK. In this role I predominantly investigated the prognostic relevance of acquired cytogenetic abnormalities of leukaemia patients in order to inform therapy.

My main research focus steams from my PhD on evaluating surrogate outcomes. Surrogates are markers that can be evaluated at an earlier time than the primary outcome of interest and inform on the treatment effect of the primary outcome. The use of valid surrogates in place of primary outcomes has huge cost and time benefits. However, the use of invalid surrogates would be extremely detrimental. Evaluating surrogates is as crucial as it is difficult, complexities in treatment mechanisms of action can mask inadequacies in a surrogates potential. Presently, surrogate evaluation researchers tend to focus on either a strict causal approach based on counterfactuals or a pragmatic meta-analytical approach using information theory and joint mixed models.

Staff, Students & Associates

Staff

Research Students

BioSS Associates