Measures of Association
Measures of Association

Measures of Association

by Raywat Deonandan, PhD

Measures of association are a group of coefficients and factors used to quantify the relationship between two or more variables. These measures are an initial step toward establishing a relationship between even seemingly unrelated events, and clinical research studies make constant use of them in their data analysis.

In this course, the student will be introduced to the methods of calculation of relative risk, associated risk, and odds ratios.

For optimal understanding, the student is required to be familiar with the basics of arithmetic, algebra, and epidemiology. Since some concepts may be very abstract to some, it’s encouraged to rewatch some video lectures until the concepts are fully grasped.

Course Details

  • Videos 7
  • Duration 0:26 h
  • Quiz questions 14
  • Concept Pages 1

Content

Your Educators of course Measures of Association

 Raywat Deonandan, PhD

Raywat Deonandan, PhD

Dr. Raywat Deonandan is a Global Health Epidemiologist and an Associate Professor of Health Sciences at the University of Ottawa, Canada.
He obtained his PhD in Epidemiology and Biostatistics from the University of Western Ontario in 2001. He is also the founder and Executive Editor of the Interdisciplinary Journal of Health Sciences, and an award-winning author and journalist.
Dr. Deonandan embraces new media to engage in public education and co-hosts a science education podcast. In 2016, he received the OCUFA Teaching Award, the highest professorial teaching honour in Ontario.
Within Lecturio, he teaches courses on Epidemiology and Biostatistics."


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Thanks
By Kushal S. on 31. October 2021 for Contingency Table – Relative Risks (Measures of Association)

Very clear, concise to understand the formulas for all usmle tests

 
Very good definitions for PAR and AR
By Keila O. on 04. March 2021 for Attributable Risk – Attributable Risk and Odds Ratio (Measures of Association)

Thanks! I just wish I could've understood RRR, RR and ARR better in the previous lecture