Dr Sajobi

Dr. Tolu Sajobi




Cumming School of Medicine, Department of Community Health Sciences


Cumming School of Medicine, Department of Clinical Neurosciences

Full Member

Hotchkiss Brain Institute


Libin Cardiovascular Institute

Contact information

Phone number

Office: 403 210 8586

Preferred method of communication



Educational Background

BSc (First Class Honors) Statistics, Obafemi Awolowo University, Ile-Ife, Nigeria, 2004

MSc Mathematical Statistics , University of Windsor, Windsor, Canada, 2008

PhD Biostatistics, University of Saskatchewan, Saskatoon, Canada, 2012



Dr. Tolu Sajobi is Professor in the Departments of Community Health Sciences and Clinical Neurosciences and Academic Director (EDI Data Research and Strategy) in the Office of Equity Diversity and Inclusion at the University of Calgary 

Dr. Sajobi is the Director of the Person-Centered Methods & Analytics  (PCMA) lab through which he leads a methodological research program with application in three areas of clinical research, namely:  1) measurement and analysis of behavioral and patient-reported outcomes; 2) classification and prediction methodologies for prognostic research and clinical decision support tools, and 3) design and analysis of randomized clinical trials. His research has found applications in several disease domain areas include neuroepidemiology, neurology, cardiology, and orthopedics. Dr. Sajobi’s research program has been supported by research grants from the MSI Foundation, Alberta Innovates Health Solutions, NSERC, and CIHR.

As a biostatistician, Dr Sajobi provides statistical leadership on a variety of (inter)national clinical and population health research projects. He has published more than 250 peer-reviewed publications and given over 100 conference presentations


Areas of Research

Biostatistics, patient-reported outcomes, clinical trials


Course number Course title Semester
MDCH 611 LEC 01 01 (VETM611)Models Hlth Outcomes 2020
MDCH 611 TUT 01 T01 (VETM611)Models Hlth Outcomes 2021
MDCH 612 TUT 01 T01 Biostatistics III 2020


Development and evaluation of a user-centered electronic outcome assessment (strokePRO) system for acute stroke trials

Stroke is a leading cause of death and long-term disability in Canada and globally. In addition to the loss of physical functioning, stroke severely impacts multiple dimensions of a patients’ quality of life (QOL). Randomized controlled trials remains the gold standard for generating Grade I evidence about the effectiveness of stroke interventions. However, outcomes assessment of acute stroke trials remains a daunting challenge for patients and trial investigators. Stroke trials typically adopt measures of functional disability, such as the modified Rankin scale (mRS), as primary outcome, but do not assess a broad range of QOL dimensions (e.g., fatigue, cognitive function, mood, mental health, etc.) which are known to be important to stroke patients. In addition, the assessment of outcomes in acute stroke trials is still predominantly done by trained personnel during routine in-person follow-up visits or via telephone. This poses several challenges, such as proxy bias and inability to collect multiple outcomes at higher frequency and for longer time periods. Recent limitations imposed by the COVID-19 pandemic have only amplified these inefficiencies. Consequently, outcome assessment in acute stroke trials remains investigator-centered, prone to proxy-bias, inefficient, and limited to short-term assessments, resulting in missed opportunities to capture patients’ perspectives about the treatment interventions. This project aims to co-design and develop the strokePRO system, a user-centered electronic clinical outcome assessment system, with patient. This system will facilitate flexible and remote collection of outcomes (in acute stroke trials) directly from the patients, from wherever they are, a need that became clear during the COVID-19 pandemic. The proposed strokePRO system will enhance patient recruitment and retention in stroke trials, improve the robustness and quality of outcomes data collected, and trial operations while reducing costs.


Funding Details

This project is funded by Alberta Innovates. 

Amount: $600,000

Duration: 2024 – 2027

Principal Investigator: Tolu Sajobi

Co-Principal Investigators: Bijoy K. Menon, Maria Santana, Michael Hill 

Characterizing patient preferences and personalizing risk information for optimal patient- centered management of coronary artery disease

Coronary artery disease (CAD) is a leading cause of disability and death in Canada. Bypass surgery and procedures using balloons and stents to open up blocked vessels are two widely used treatment options for patients with multivessel CAD, and have been shown to improve patient outcomes compared to therapy with medications alone. Clinical practice guidelines have recommended surgery for managing multi-vessel disease in most cases, but increasingly suggest a shared-decision making approach. This is because there is substantial uncertainty about the best treatment approach for stable multivessel disease for many patients, especially in the elder and those living with multimorbidity, which are associated with higher procedural complication risks. Consequently, treatment decisions for these patients with CAD are usually based on Heart Team discussions, which involve discussions among clinical experts about the trade-offs in outcomes associated with different treatment strategies, variability in levels of accepted risk by patients, and institutional clinical practice cultures. These Heart Team discussions are intended to arrive at the best decision for each patient, but explicit risk information is rarely provided to patients nor are patient preferences formally incorporated within the current decision-making process. There is a limited understanding of patients’ preferences towards the different treatment options for patients with stable multivessel disease and how patients' preferences influence shared decision- making for disease management. This study will elicit patient preferences towards these treatment options and develop personalized approaches to communicate the benefit and risk associated with each treatment option to patients. The knowledge gained through this research will be used to support processes and practices for shared decision-making between patients and Heart Teams and ensure delivery of the right treatment to the right patient at the right time.


Funding Details

This project is jointly funded by the Canadian Institutes of Health Research and  Alberta Innovates. 

Amount: $1,018,000

Duration: 2023 – 2027

Principal Investigator: Tolu Sajobi

Co-Principal Investigators: Stephen Wilton, Matthew James, and Michelle Graham

Unsupervised machine learning algorithms for patient-reported outcomes measures

Patient-reported outcome measures (PROMs), which are designed to be used by patients to describe their health status, including their quality of life, can be used by clinicians and healthcare decision makers to inform the delivery of care. In order to contribute to improving patient care, PROMs must be valid (i.e., measure what they are supposed to measure) and reliable (i.e., measure the same thing over time and for patients with different characteristics). Sometimes, patient responses on PROMs are not consistent with what was expected. These inconsistencies may occur because patients with different characteristics may not understand or interpret questions about their health in the same way (differential item functioning), or their interpretations may change over time (response shift).  This project focuses on data-driven machine-learning methods to efficiently cluster (i.e., group) patients with similar patterns of differential item functioning or response shifts. Computer simulations and real world data will be used to compare these methods.


Funding Details 

This research is funded by the Canadian Institutes of Health Research

Amount: $367,200

Duration: 2020 - 2024

Principal Investigators: Tolu Sajobi & Lisa Lix


  • Mid-Career Research Leader Award, O’Brien Institute for Public Health, University of Calgary. 2022
  • Outstanding Article of the Year Award, Third Place (with Dr. Richard Sawatzky and others), The use of latent variable mixture models to identify invariant items in test construction, International Society for Quality of Life. 2019
  • Calgary's Top 40 under 40, Avenue Calgary Magazine . 2018
  • GREAT Supervisor Award, University of Calgary. 2018
  • New Investigator Oral Presentation Award, International Society for Quality of Life Research. 2011
  • Vanier Graduate Scholar, Canadian Institutes for Health Research . 2009
  • Best Graduating Honors Student in Mathematics, Obafemi Awolowo University. 2004