In the first chapter, we introduce the concept of survival analysis, explain the importance of this topic, and provide a quick introduction to the theory behind survival curves. We discuss why special methods are needed when dealing with time-to-event data and introduce the concept of censoring. We also discuss how we describe the distribution of the elapsed time until an event.

The term "survival analysis"

Introducing the GBSG2 dataset

What will this course cover?

Why learn survival methods?

Digging into the GBSG2 dataset 1

Using the Surv() function for GBSG2

The UnempDur dataset

Measures used in survival analysis

Interpreting a survival curve I

Interpreting a survival curve II

Interpreting a survival curve III

What is Survival Analysis?

In this chapter, we will look into different methods of estimating survival curves. We will discuss the Kaplan-Meier estimate and the Weibull model as tools for survival curve estimation and learn how to communicate those results through visualization. 

Kaplan-Meier estimate

Function to compute the Kaplan-Meier estimate

First Kaplan-Meier estimate

When does the Kaplan-Meier curve drop?

Why use Kaplan-Meier

Understanding and visualizing Kaplan-Meier curves

Exercise ignoring censoring

Estimating and visualizing a survival curve

The Weibull model for estimating survival curves

Estimating median survival from a Weibull model

Survival curve quantiles from a Weibull model

Estimating the survival curve with survreg()

Visualizing the results of Weibull models

Comparing Weibull model and Kaplan-Meier estimate

Estimation of survival curves

In this chapter, we will learn how to estimate and visualize a Weibull model to learn about the effects of covariates on the time-to-event outcome.

Why use the Weibull model

Interpreting coefficients

Compute Weibull model

Visualizing Weibull models

ggsurvplot() versus ggsurvplot_df()

Computing a Weibull model and the survival curves

Visualising a Weibull model

Plotting options

Other distributions than Weibull

survreg() arguments

Computing a Weibull and a log-normal model

Comparing Weibull and Log-Normal Model I

Comparing Weibull and Log-Normal Model II

The Weibull model

In the last chapter, we learn how to compute and interpret Cox models to understand why they are useful and how they differ from Weibull models.

Model Cox

Menghitung model Cox

Asumsi proportional hazards

Memvisualisasikan model Cox

Menghitung kurva survival dari model Cox

surv_summary()

Apa yang telah kita pelajari

Mengapa "pasien imajiner"?

Capstone: Model Cox

Proyek Akhir: Membandingkan kurva survival

Selamat tinggal

The Cox Model

Apakah pasien yang mengonsumsi obat baru bertahan hidup lebih lama daripada yang lain? Seberapa cepat orang mendapatkan pekerjaan baru setelah menganggur? Apa yang dapat saya lakukan agar teman-teman tetap berada di lantai dansa saat pesta saya? Semua pertanyaan ini memerlukan analisis data waktu-ke-kejadian, yang menggunakan metode statistik khusus. Kursus ini memperkenalkan konsep dasar analisis data waktu-ke-kejadian, yang juga disebut analisis survival. Pelajari cara menangani data waktu-ke-kejadian serta cara menghitung, memvisualisasikan, dan menafsirkan kurva survival serta model Weibull dan Cox.

Introduction to Regression in R

Pelajari data waktu-ke-kejadian, seperti kematian atau mendapat pekerjaan. Estimasi dan interpretasi model!

Analisis Survival di R

Ahli Statistik di R

Mengapa "pasien imajiner"?

Analisis Survival di R

Latihan interaktif praktis