This is a basic course in epidemiology which also covers a variety of analytic topics not commonly addressed in elementary statistics courses. This course will introduce students to the field of epidemiology. Students will critically evaluate articles in the epidemiologic literature and examine epidemiologic methods including:
- data collection
- study design and statistical analysis
- relative risk
- contingency tables
- logistic and Poisson
- measurement error and exposure misclassification
- imputation of missing values
- multilevel regression models in epidemiology
- Explain basic concepts in descriptive epidemiology such as incidence, prevalence, mortality, morbidity and effect.
- Distinguish among common epidemiological designs such as case-control, cohort, cross-sectional and randomized controlled studies.
- Identify major categories of bias that can affect the validity of epidemiological studies.
- Apply common measures of association such as relative risk, odds and odds ratios, attributable risk, attributable risk percentage and population attributable risk to epidemiological data.
- Employ common tools of epidemiological statistics such as logistic and Poisson regression and become oriented to multilevel regression models to analyze appropriate data.
- Admission to the PSC in Population Health Data Analysis or permission of the Faculty Advisor.
- Working knowledge of basic statistical concepts (how to interpret P values, null hypothesis, and confidence intervals).
- Some experience with regression modelling and basic SAS programming (e.g., DATA step and procedures).
Instructor: Mohsen Soltanifar
Mohsen Soltanifar, PhD(biostat), MSc(biostat), MSc(math) is currently a Senior Biostatistician at Cytel Canada Vancouver office. His main area of expertise is statistical inference and biostatistical methodology development in neurology, clinical trials and public health. His current responsibilities include biostatistical methodology development, manuscript publication in journals and poster presentation in international pharmaceutical conferences in the areas of network meta-analysis, Bayesian adaptive trials, and simulations. In addition, he serves as consulting biostatistician for various clients. He brings eight years’ experience of data analysis and biostatistical methodology development at the time of joining University of Victoria Division of Continuing Studies.
Prior to joining University of Victoria Division of Continuing Studies, he has served as instructor at the University of Toronto (2017-2021), and research assistant in three institutions including the SickKids Hospital in Toronto (2015-2020), Canadian Center for Health and Safety in Agriculture in Saskatoon (2014), and Saskatchewan Population Health Research Evaluation Unit in Saskatoon(2012-2014). His developed skills in the mentioned positions include data analysis of various designs, statistical programming with BUGS, R, SAS, STATA and SPSS, presentation at various national and international statistical conferences.
He attended University of Toronto and received a PhD in biostatistics (2020). Prior to that, he attended University of Saskatchewan and received MSc in biostatistics (2014) and MSc in mathematics (2011).
This program has been developed by Population Data BC in partnership with the Division of Continuing Studies, University of Victoria.
For further details, visit the Division of Continuing Studies webpage.