Health Research Methods, Evidence, and Impact


Eleanor Pullenayegum

BA Hons (Cambridge), CASM (Cambridge), PhD (Toronto)

Associate Professor (Part-time), Department of Health Research Methods, Evidence, and Impact

Eleanor.pullenayegum@sickkids.ca

 

 

 

 

Academic Interests

My research falls into two broad categories: statistical methods in health economics, and methods for longitudinal data.

In health economics I am interested in the analysis of cost and health utility data, and focus particularly on semi-parametric models that avoid the need to model the unusual distributions of these outcomes.

My work on longitudinal data focuses particularly on the case where the measurement times are neither completely deterministic nor completely random. This might occur, for example, with chart reviews. Ignoring the informative nature of the visit times will lead to misleading inferences. My current work is comparing inverse-probability weighting, binning, analysis of the increments of the process, and a doubly robust estimator.

Current HRM Graduate Students:
Qing Guo (PhD, Biostatistics)

Recent HRM Graduate Students:
Nina Raju (MSc)

Recent Math & Stats Graduate Students:
Hoi Suen Wong (MSc)

Selected Publications

  1. Pullenayegum EM, Wong HS*, Childs A. Generalized Additive Models for analysing EQ-5D utility data. Medical Decision Making (in press).
  2. Guo Q*, Hall G, McKinnon M, Thabane L, Pullenayegum EM. Setting Sample Size Using Cost-Efficiency in fMRI Studies. Journal of Open Access Medical Statistics 2012: 2:33-41.
  3. Pullenayegum EM. Empirical Priors for Between-Study Heterogeneity in Bayesian Meta-analysis. Statistics in Medicine 2011 30(26):3082-3094.
  4. Pullenayegum EM, Willan AR. Marginal Models for Censored Longitudinal Cost Data: Appropriate Working Variance Matrices in Inverse-Probability-Weighted GEEs Can Improve Precision. International Journal of Biostatistics Vol.7:Iss.1, Article 14. Doi:10.2202/1557-4679.1170.
  5. Pullenayegum EM, Tarride JE, Xie F, O'Reilly D. Calculating utility decrements associated with an adverse event: marginal Tobit and CLAD coefficients should be used with caution. Medical Decision Making. Med Decision Making 2011 Nov;31(6):790-9 PMID: 22067429..
  6. Pullenayegum EM, Cook RJ. The analysis of treatment effects for recurring episodic conditions. Statistics in Medicine 2010 Jun30;29(14):1539-58. PubMed PMID: 20535764.
  7. Pullenayegum EM, Tarride J-E, Xie F, Goeree R, Gerstein H, O'Reilly D. Analysis of health utility data when some subjects attain the upper bound of 1: are Tobit and CLAD models appropriate? Value in Health. 2010 Jun-Jul;13(4):487-94. Epub 2010 Mar 10. PubMed PMID:20230549.
  8. Pullenayegum EM, Lam C, Manlhiot C, Feldman BM. Fitting Marginal Structural Models: Estimating covariate-treatment associations in the re-weighted dataset can guide model fitting. Journal of Clinical Epidemiology 2008; 61(9): 875-881.
  9. Pullenayegum EM, Willan AR. Semi-parametric models for cost-effectiveness analysis: improving the efficiency of estimation from censored data. Statistics in Medicine 2007; 26(17): 3274-3299.
  10. Pinto EM, Willan AR, O'Brien BJ. Cost-effectiveness analysis for multinational clinical trials. Statistics in Medicine 2005; 24: 1965-1982.

* indicates student under my supervision

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