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Survival Analysis in R

Intermediate
4.2+
15 reviews
Updated 12/2024
Learn to work with time-to-event data. The event may be death or finding a job after unemployment. Learn to estimate, visualize, and interpret survival models!
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RProbability & Statistics4 hours14 videos50 exercises3,650 XP12,357Statement of Accomplishment

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Course Description

Do patients taking the new drug survive longer than others? How fast do people get a new job after getting unemployed? What can I do to make my friends stay on the dancefloor at my party? All these questions require the analysis of time-to-event data, for which we use special statistical methods. This course introduces basic concepts of time-to-event data analysis, also called survival analysis. Learn how to deal with time-to-event data and how to compute, visualize and interpret survivor curves as well as Weibull and Cox models.

Prerequisites

Introduction to Regression in R
1

What is Survival Analysis?

Start Chapter
2

Estimation of survival curves

Start Chapter
3

The Weibull model

Start Chapter
4

The Cox Model

Start Chapter
Survival Analysis in R
Course
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*4.2
from 15 reviews
47%
33%
20%
0%
0%
  • Thashalak A.
    19 days

    Amazing course, highly recommend

  • Dimitris L.
    10 months

    nice course

  • Nicolas F.
    10 months

    This was a fantastic course to learn a package that is capable of producing great analyses typically applicable in the medical sciences.

  • Edwin A.
    10 months

    Through this course I have learned to work with time-to-event data, to estimate using Kaplan-Meier estimate, visualize and interpret survival models, such as Weibull and Cox model.

  • Inaamul H.
    about 1 year

    The course was a good introduction to survival analysis. The best part of DataCamp courses is that you get to use the code and try the analysis in the finely built exercises.

"Amazing course, highly recommend"

Thashalak A.

"nice course"

Dimitris L.

"This was a fantastic course to learn a package that is capable of producing great analyses typically applicable in the medical sciences."

Nicolas F.

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