About this Course
The final course in the program introduces the principles and methods of statistical inference for categorical data and censored survival data. You’ll learn to use R to load, manipulate and analyze categorical and binary data. We’ll construct contingency tables and use R to calculate various risk measures and confidence intervals around those measures. You’ll learn to fit logistic regression models to data, obtain model coefficients and odds ratios, and formulate and test hypotheses about them. We’ll use product limit measures (Kaplan-Meier), Cox regression models and log-rank tests to calculate confidence measures, perform hypothesis tests and draw conclusions in a variety of cases.
Prerequisites:
What You’ll Learn
- How to analyze binary outcome data using risk differences, relative risk and odds ratios
- How to perform hypothesis testing and obtain confidence intervals for binary outcome data using logistic regression models
- How to perform hypothesis testing and obtain confidence intervals for time to event data using Cox regression models
- How to distinguish between and perform exploratory and confirmatory analyses
- How to interpret and critique the results of applications of statistical techniques found in the health science literature
Get Hands-On Experience
You'll complete a data analysis project using real-world examples to manipulate data, display and report results, and draw appropriate conclusions.