Traumatic Brain Injury – Dalla Lana School of Public Health (U of T)- [PDF]

150 150 Office of Research Trainees

Area of Research: Traumatic brain injury (TBI), defined as “an alteration in brain function, or other evidence of brain pathology, caused by an external force” is among the most serious and disabling neurological disorders affecting adults and children in all societies. Recently endorsed is the view of TBI as a chronic disease process encompassing clinical, pathological and cellular changes starting at the time of the head injury event. As a result, TBI has the potential to affect various organs and bodily systems and cause and/or expedite various disease progressions. Our study seeks to add to the understanding of outcomes relevant to patients and clinicians (i.e., functional outcome, all-cause mortality), and those of interest to social security (i.e., resource utilization). The Institute for Clinical Evaluative Sciences (ICES) houses high quality administrative data on a wide variety of publicly funded services provided, including individual-level information on databases from emergency departments, acute care, inpatient rehabilitation, community services and long-term care, continuing care, mortality, and prescription data within the province. These datasets provide a rare opportunity to study resource consumption, all-cause mortality and functional outcomes for TBI persons across the continuity of care taking into account socio-demographic, clinical and injury-related profiles of persons with TBI of varying severities. Further, it allows for the application of innovative statistical techniques to advance this research.


  • PhD awarded within the past 5 years in biostatistics (or related degree) with experience in applied statistical collaboration in health care research
  • Thorough understanding of statistical principles and biostatistical methods (includes proficiency in logistic regression, Poisson regression and other GLM models)
  • Conversant with SAS and R programming
  • Strong ability to communicate findings to an interdisciplinary team
  • Understanding of Bayesian nonparametric or Bayesian semiparametric methods and/or machine learning
    techniques considered an asset
  • Previous experience working with ICES data or other last administrative datasets would also be considered an asset

Length of Time: 1 years with potential for extension depending on funding

Closing Date: May 17, 2017

For further details regarding the position and to apply please see here.