Survival-analysis-in-R

Coursera: Survival analysis in R for public health - Alex Bottle (Imperial College London)

This is an introductory course. Learning objectives of the course are

Week 1

1. Define survival analysis
2. Explain when it is valid to use survival analysis
3. Explain and run Kaplan-Meier plot and log-rank test in R and interpret the results

Week 2

1. Define a hazard in the context of survival analysis
2. Run a simple Cox model in R and interpret the output
3. Select and apply appropriate methods to formulate and examine statistical associations between variables within a data set in R

Week 3

1. Explain and run a multiple Cox model in R and interpret the output
2. Assess the potential effect of correlated variable on modelling
3. 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
4. Test for non-convergence in a regression model and fix the problem; Recognise that approaches other than Cox exist for survival analysis

Week 4

1. Evaluate the model assumptions for Cox regression in R
2. Apply a simple way to fix the problem of proportionality assumption not met
3. Apply and critique simple ways to deal with missing values in a predictor in a regression model
4. Describe and compare some common ways to choose a multiple regression model