Exercise ignoring censoring

You throw a party and at 1 a.m. guests suddenly start dancing. You are curious to analyze how long your guests will dance for and start collecting data. The problem is that you get tired and go to bed after a while.

You obtain the following right censored dancing times data given in dancedat:

name is the name of your friend.
time is the right-censored dancing time.
obs_end indicates if you observed the end of your friends dance (1) or if you went to sleep before they stopped dancing (0).

You start analyzing the data in the morning, but you are tired and, at first, ignore the fact that you have censored observations. Then you remember this course on DataCamp and do it correctly.

The survival package is loaded for you in this exercise.

This exercise is part of the course

Survival Analysis in R

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Exercise instructions

Estimate the survival function pretending that all censored observations are actual observations.
Estimate the survival function from this dataset via Kaplan-Meier.
Plot the correct and wrong survival curves two using ggsurvplot_combine() and compare them. Notice how they differ.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Create dancedat data
dancedat <- data.frame(
  name = c("Chris", "Martin", "Conny", "Desi", "Reni", "Phil", 
    "Flo", "Andrea", "Isaac", "Dayra", "Caspar"),
  time = c(20, 2, 14, 22, 3, 7, 4, 15, 25, 17, 12),
  obs_end = c(1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0))

# Estimate the survivor function pretending that all censored observations are actual observations.
km_wrong <- survfit(___(time) ~ 1, data = dancedat)

# Estimate the survivor function from this dataset via kaplan-meier.
km <- survfit(___(___, ___) ~ ___, data = dancedat)

# Plot the two and compare
ggsurvplot_combine(list(correct = ___, wrong = ___))

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