- Email Address(es)
- Office Phone
- 416-813-7654 x301031
- Office Address
- Hospital for Sick Children 555 University Avenue Toronto, ON M5G 1X8
- Hospital for Sick Children
- Biostatistics Division
- Associate Professor
- SGS Status
- Full Member
- Appointment Status
- Status Only
I help ensure that healthcare is based on high-quality evidence by:
- developing new statistical methods to handle the complex data that often arise in medical research
- working with colleagues to choose the most appropriate design and analysis for research
- training graduate students in statistical methods
I have several projects in these areas that are suitable for students considering Master’s or Doctoral work; please contact me if interested.
Education & Training History
PhD, Biostatistics, University of Toronto
Certificate of Advanced Studies in Mathematics, University of Cambridge
BA, Mathematics, University of Cambridge
Scientist, The Hospital for Sick Children
Primary Teaching Responsibilities
Statistical Analysis of Health Economic Data (Summer 2016)
Professional Summary & Appointments
Scientist, Child Health Evaluative Sciences, Hospital for Sick Children (2013-present)
Associate Professor, Dalla Lana School of Public Health, University of Toronto (2013-present)
Biostatistician, St Joseph’s Healthcare Hamilton (2007-2013)
Associate Professor, Depeartment of Clinical Epidemiology & Biostatistics, McMaster University (2012-2013)
Assistant Professor, Depeartment of Clinical Epidemiology & Biostatistics, McMaster University (2007-2012)
Postdoctoral Fellow, Department of Statistics and Actuarial Science, University of Waterloo (2006)
Medical Statistician, Centre for Applied Medical Statistics, Department of Public Health and Primary Care, University of Cambridge (2000-2002))
Honours & Awards
CIHR New Investigator Award (2012-2017)
Young Investigator Award of the Section on Teaching Statistics in the Health Sciences, American Statistical Association (2008)
Schuldham Plate, Gonville & Caius College, University of Cambridge (1999)
Current Research Projects
- Methodology for irregularly observed longitudinal data
Longitudinal data are useful for understanding how disease evolves over time. Often longitudinal data can be collected through clinic based cohorts in which patients are enrolled in the cohort at diagnosis, followed up as medically necessary, and data are gathered through a chart review. This is an efficient and low cost approach to data collection. However, since patients tend to visit more often when unwell, this can lead to overestimation of the burden of disease unless accounted for appropriately. I develop analytic methods to handle the informative nature of the visit process.
- Methodology for health utilities
Health utilities are used in economic evaluations to help assess cost-effectiveness of treatments, and so ultimately contribute to decisions on which treatments should be publicly funded. I am interested in measurement of health utilities, in particular a) correctly quantifying the statistical uncertainty in these measurements, and b) reducing the extent of uncertainty. This is important as it reduces the risk of funding treatments that are not cost-effective, this enabling better use of limited resources.
Pullenayegum EM. Multiple outputation for the analysis of longitudinal data subject to irregular observation. Stat Med. 2015 Dec 13. doi: 10.1002/sim.6829. [Epub ahead of print] PubMed PMID: 26661690.
Pullenayegum EM, Platt RW, Barwick M, Feldman BM, Offringa M, Thabane L. Knowledge translation in biostatistics: a survey of current practices, preferences, and barriers to the dissemination and uptake of new statistical methods. Stat Med. 2016 Mar 15;35(6):805-18. doi: 10.1002/sim.6633. Epub 2015 Aug 25. PubMed PMID: 26307183.
Pullenayegum EM, Chan KKW*, Xie F. Quantifying Parameter Uncertainty in EQ-5D-3L Value Sets and Its Impact on Studies That Use the EQ-5D-3L to Measure Health Utility: A Bayesian Approach. Med Decis Making. 2016 Feb;36(2):223-33. doi: 10.1177/0272989X15591966. Epub 2015 Jul 2. PubMed PMID: 26139449.
Pullenayegum EM, Lim LS. Longitudinal data subject to irregular observation: A review of methods with a focus on visit processes, assumptions, and study design. Stat Methods Med Res. 2014 May 21. pii: 0962280214536537. [Epub ahead of print] PubMed PMID: 24855119.
Pullenayegum EM, Perampaladas K, Gaebel K, Doble B, Xie F. Between-country heterogeneity in EQ-5D-3L scoring algorithms: how much is due to differences in health state selection? Eur J Health Econ. 2015 Nov;16(8):847-55. doi: 10.1007/s10198-014-0633-1. Epub 2014 Sep 25. PubMed PMID: 25252970.
Chan KKW*, Gupta M, Willan A, Pullenayegum E. Underestimation of Uncertainties in Health Utilities derived from Mapping Algorithms involving Health Related Quality of Life Measures: Statistical Explanations and Potential Remedies. Medical Decision Making Med Decis Making. 2014 Oct;34(7):863-72. doi: 10.1177/0272989X13517750. PubMed PMID: 24407513.
Guo Q*, Thabane L, Hall G, McKinnon M, Goeree R, Pullenayegum E. A systematic review of the reporting of sample size calculations and corresponding data components in observational functional magnetic resonance imaging studies. Neuroimage. 2014 Feb 1;86:172-81. doi: 10.1016/j.neuroimage.2013.08.012. Epub 2013 Aug 15. Review. PubMed PMID: 23954487.
Pullenayegum EM, Feldman BM. Doubly robust estimation, optimally truncated inverse-intensity weighting and increment-based methods for the analysis of irregularly observed longitudinal data. Stat Med. 2013 Mar 15;32(6):1054-72. doi:
10.1002/sim.5640. PubMed PMID: 23047604.
Pullenayegum EM. Adaptive Bayesian randomized trials: realizing their potential. J Bone Joint Surg Am. 2012 Jul 18;94 Suppl 1:29-33. doi:10.2106/JBJS.L.00094. PubMed PMID: 22810444.
Pullenayegum EM. An informed reference prior for between-study heterogeneity in meta-analyses of binary outcomes. Stat Med. 2011 Nov 20;30(26):3082-94. doi:
10.1002/sim.4326. PubMed PMID: 22020726.
Pullenayegum EM, Cook RJ. The analysis of treatment effects for recurring episodic conditions. Stat Med. 2010 Jun 30;29(14):1539-58. doi: 10.1002/sim.3882.
PubMed PMID: 20535764.
* indicates graduate student under my supervision
See https://www.researchgate.net/profile/Eleanor_Pullenayegum for a more complete list.