### 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.

**For Registration Form scroll below. **

**Courses: **

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**Introduction to R for health researchers **

**Monday, February 26, 2018, 9 am — 4 pm Basic use of R and R Studio **

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**Introduction to R for regression analysis **

** Monday, April 16, 2018, 9 am — 4 pm Models for continuous, binary and count data **

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**Introduction to R for survival analysis **

** Wednesday, May 23, 2018, 9 am — 4 pm Handling time-to-event data **

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**Instructors: **

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**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.

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**Fees, Registration, Contact Information & Location Details **

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**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 **

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**Single session: **

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**$ 200 (includes lunch & snacks) **

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**Multiple sessions discount: **

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**$300 for 2 sessions **

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**$450 for all 3 sessions **

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**Registration **

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**Complete the Registration Form . After submission, you will proceed to payment page. **

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**For more information contact: **

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**email: beena.vyas@thebru.ca**

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**Introduction to R for Health Researchers **

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**Monday February 26, 2018, 9 am — 4 pm **

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**Primary Instructor: George Tomlinson, PhD **

**Secondary Instructor: John Matelski, MSc **

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**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**

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**Introduction to R for Regression Analysis **

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**Monday, April 16, 2018, 9 am — 4 pm **

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**Primary Instructor: Nicholas Mitsakakis, PhD **

**Secondary Instructor: John Matelski, MSc **

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**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 **

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**Introduction to R for Survival Analysis **

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**Wednesday May 23, 2018, 9 am — 4 pm **

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**Primary Instructor: Ella Huszti, PhD **

**Secondary Instructor: Leah Szadkowski, MSc **

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**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)**

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