Coursera: Survival analysis in R for public health - Alex Bottle (Imperial College London)
This is an introductory course. Learning objectives of the course are
- Define survival analysis
- Explain when it is valid to use survival analysis
- Explain and run Kaplan-Meier plot and log-rank test in R and interpret the results
- Define a hazard in the context of survival analysis
- Run a simple Cox model in R and interpret the output
- Select and apply appropriate methods to formulate and examine statistical associations between variables within a data set in R
- Explain and run a multiple Cox model in R and interpret the output
- Assess the potential effect of correlated variable on modelling
- Describe a given data set from scratch, including data item features and data quality issues, using descriptive statistics and simple graphical methods as a necessary first step for more advanced analysis using R software
- Test for non-convergence in a regression model and fix the problem; Recognise that approaches other than Cox exist for survival analysis
- Evaluate the model assumptions for Cox regression in R
- Apply a simple way to fix the problem of proportionality assumption not met
- Apply and critique simple ways to deal with missing values in a predictor in a regression model
- Describe and compare some common ways to choose a multiple regression model