**Learning Objectives**

- Define main types of censoring
- Define the assumption of uninformative censoring
- Define survival function, hazard functions, cumulative event function
- Perform a Kaplan-Meier estimate
- Perform, interpret, and identify assumptions of the logrank test
- Define and calculate potential follow-up time
- Calculate median survival time

**Outline**

- Introduction to censored data
- Outcome variable: time-to-event
- Types of censored data
- Assumption of uninformative censoring

- Survival function and Kaplan-Meier estimator
- Comparing groups: Log-rank test

- Vittinghoff sections 3.1-3.5
- Tutorial Paper
*Survival Analysis Part I: Basic concepts and first analyses*by Clark, Bradburn, Love, Altman. British Journal of Cancer (2003) 89, 232 – 238

**Learning Objectives**

- Calculate Kaplan-Meier estimates of survival probability over time
- Plot survival curves for censored time-to-event data
- Perform and interpret log-rank test
- Define “informative” censoring

**Exercises**

- Calculate the follow-up table for 6 MP patients in the leukemia study
- Plot the Kaplan-Meier estimate of the follow-up table from 1.
`library(survminer)`

is recommendable. - What is the 75th percentile of survival times for the 6 MP group? For the Placebo group? This is the time that 75% of the patients survive.
- Suppose you were instructed to cap follow-up times at 20 weeks. Re-do the Kaplan-Meier plot for both groups, and re-do the logrank test.
- Give a hypothetical example of how censoring in this example might be “informative.”