BIOSTATISTICS UNIT

CENTRE FOR EVALUATION OF MEDICINES
105 Main St. East, Level P1
Hamilton, Ontario, Canada  L8N 1G6

 
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Eleanor Pullenayegum PhD


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ASSISTANT PROFESSOR
Department of Clinical Epidemiology and Biostatistics
McMaster University

BIOSTATISTICIAN
Centre for Evaluation of Medicines
St. Joseph's Healthcare

Contact Information:

Biostatistics/FSORC
St. Joseph's Healthcare Hamilton
50 Charlton Avenue East, Room H321
HAMILTON ON L8N 4A6 CANADA

Phone: 905-522-1155 x35929
Fax: 905-308-7212

email: pullena@mcmaster.ca

                                                         Brief Biography   Research Interests   

Brief Biography

Eleanor Pullenayegum is Assistant Professor in the Department of Clinical Epidemiology and Biostatistics at McMaster University and a Biostatistician in the Biostatistics Unit at the Father Sean O'Sullivan Research Centre, St. Joseph's Healthcare Hamilton--a Division of St. Joseph's Health System. She received a PhD in Biostatistics from the University of Toronto, then spent a year as a Post-Doctoral Fellow at the Department of Statistics and Actuarial Sciences at the University of Waterloo. Before coming to Canada she did a BA in Mathematics at Gonville and Caius College, University of Cambridge, followed by the Certificate of Advanced Studies in Mathematics, also at Cambridge. She then worked as a Research Assistant and consulting statistician for the Centre for Applied Medical Statistics at the Department of Public Health and Primary Care, University of Cambridge.

Research Interests

Eleanor is interested in developing statistical methodology for healthcare research. A particular area of interest is in semi-parametric regression models in the presence of incomplete data. An example where this methodology is used is in estimating cost-effectiveness, where we often need to fit a linear regression model for mean cost, without making additional distributional assumptions. In this example, incompleteness arises from right-censoring. A more surprising example occurs when fitting marginal structural models, in which the counterfactual outcomes are, by definition, unobserved. She is interested in comparing estimation techniques in terms of their bias, efficiency and practicality.

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