This course is designed to give clinical epidemiology students' knowledge and skills in statistical methods that apply to clinical epidemiology. Students will acquire working experience in applying these methods to datasets, analysing epidemiological data, and interpreting findings. As well, students will develop statistical writing skills and learn how to present results to assist them with future research publications.

For each statistical method, this course will be focused in teaching: "what is it" and "how to do it." Topics covered in this course include: data types, measures of central tendency, measures of variability, testing for the difference between two groups (analysis of means, rates, and proportions), constructing 95% confidence intervals, nonparametric analyses sample size and study power estimation, testing for trend, analysis of variance, analysis of covariance, simple and multiple linear regression, logistic regression, survival analysis-life table and Kaplan-Meier curves, log-rank tests, and Cox proportional hazards models. The final part of the course focuses on how to build a good multivariable model by assessing details such as the number of variables allowed and statistical fit. Computing is also part of this course. Knowledge in SAS or other equivalent statistical packages (such as SPSS, STATA, MINITAB etc.) is a prerequisite of the course. Students are recommended to get training in a statistical packages (SAS) prior to taking this course.

Objectives: 1) To learn about data types, measures of central tendency, 95% confidence intervals, measures of variability, and both parametric and nonparametric tests of differences between two groups. 2) To learn how to compare three or more groups using tests such as analysis of variance and analysis of co-variance. 3) To learn how to calculate sample size and statistical power for a study. 4) To carry out multiple linear regression, logistic regression, and survival analysis. 5) How to build a good multivariable model by assessing details such as the number of variables allowed and statistical fit. 6) To use SAS statistical package for data analysis.