2018 Spring R Course Series
R is a powerful and flexible software environment for statistical analysis and graphics. R is freely available, facilitating analysis and model transparency and reproducibility. Over recent years, it has become increasingly popular among health researchers.
Participant experience
Participants will be expected to have some basic knowledge of statistics but are not expected to be familiar with R.
Registration for these courses is closed.
Courses:
Introduction to R for health researchers
Monday, February 26, 2018, 9 am — 4 pm Basic use of R and R Studio
Introduction to R for regression analysis
Monday, April 16, 2018, 9 am — 4 pm Models for continuous, binary and count data
Introduction to R for survival analysis
Course cancelled (please check back for Fall 2018 dates)
Wednesday, May 23, 2018, 9 am — 4 pm Handling time-to-event data
Instructors:
George Tomlinson, MSc, PhD, Biostatistician, Director of the Biostatistics Research Unit, University Health Network, Toronto; Associate Professor at the Institute of Health Policy, Management and Evaluation, University of Toronto.
Nicholas Mitsakakis, MSc, PhD, Senior Biostatistician at Biostatistics Research Unit (BRU) at University Health Network, Assistant Professor at the Institute of Health Policy, Management and Evaluation, University of Toronto.
Ella Huszti , MSc, PhD, Senior Biostatistician at Biostatistics Research Unit (BRU) at University Health Network, Toronto.
John Matelski, MSc, Biostatistician at the Biostatistics Research Unit, University Health Network, Toronto.
Jin Ma, MSc, PhD, Biostatistician at the Biostatistics Research Unit, University Health Network, Toronto.
Leah Szadkowski , BSc, MSc, Biostatistician at Biostatistics Research Unit (BRU) at University Health Network, Toronto.
Fees, Registration, Contact Information & Location Details
Registration for these courses is closed.
The course will be held at:
Toronto Health Economics and Technology Assessment (THETA) Collaborative University Health Network Toronto General Hospital Eaton Building, 10th Floor, Room 24, 200 Elizabeth Street, Toronto, ON M5G 2C4
Single session:
$ 200 (includes lunch & snacks)
Multiple sessions discount:
$300 for 2 sessions
$450 for all 3 sessions
Registration
Complete the Registration Form. After submission, you will proceed to payment page.
For more information contact:
email: beena.vyas@thebru.ca
Introduction to R for Health Researchers
Monday February 26, 2018, 9 am — 4 pm
Primary Instructor: George Tomlinson, PhD
Secondary Instructor: John Matelski, MSc
Getting to know R and R Studio
- The R Studio programming environment
- R script files
- Objects, assignment, and functions
- Vectors and data frames
- Reading in data and saving results
Basic R Graphics
- Boxplots
- Histograms
- Scatterplots
- Adding colours and informative labels to figures
Useful Statistical Tests and Procedures
- Categorical data: Binomial, Chi-squared, and Fisher’s Exact tests
- Continuous data: t-test and non-parametric tests
- Sample size calculations
Introduction to R for Regression Analysis
Monday, April 16, 2018, 9 am — 4 pm
Primary Instructor: Nicholas Mitsakakis, PhD
Secondary Instructor: John Matelski, MSc
How to run widely-used regression models for three types of outcomes
- continuous
- binary
- count
How to output and obtain the key results
- parameter estimates
- confidence intervals for parameters
- global measures of model fit
- p-values
How to assess quality of the model fit
- diagnostics plots
- goodness of fit statistics
How to compare different models
- nested models
- non-nested models
How to obtain predictions from a fitted model
How to validate a model using the bootstrap
Introduction to R for Survival Analysis
Course cancelled (please check back for Fall 2018 dates)
Wednesday May 23, 2018, 9 am — 4 pm
Primary Instructor: Ella Huszti, PhD
Secondary Instructor: Leah Szadkowski, MSc
Basic Concepts
- Censoring
- Survival function
- Hazard function
Descriptive analyses
- Kaplan-Meier Curves
- Survival estimates
Proportional Hazards Model
- Data preparation
- Checking the proportional hazards assumption
- Cox proportional hazards model
Time Dependent Covariates
- Concepts
- Preparing data with a time dependent covariate
- Using a time dependent covariate in a Cox proportional hazards model
Competing Risks Models
- Concepts
- Modeling the cause-specific hazard of the outcome
- Modeling the cumulative incidence function (i.e., Fine & Gray models)
