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Kursus

Analisis Survival dengan Python

LanjutanTingkat Keterampilan

Diperbarui 06/2024

Gunakan analisis kelangsungan hidup untuk mengolah data waktu hingga kejadian dan memprediksi waktu kelangsungan hidup.

Mulai Kursus Gratis

PythonProbability & Statistics

4 jam

16 videos

48 Latihan

3,850 XP

5,763

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Deskripsi Kursus

Berapa lama gejala flu muncul setelah terpapar? Bagaimana jika Anda tidak tahu kapan seseorang tertular virus? Apakah gaji dan keseimbangan kerja-hidup memengaruhi kecepatan pergantian karyawan? Banyak tantangan dunia nyata memerlukan analisis survival untuk mengestimasi waktu hingga suatu kejadian secara andal, sehingga kita dapat menarik wawasan dari distribusi waktu-ke-kejadian. Kursus ini memperkenalkan konsep dasar analisis survival. Melalui praktik langsung, Anda akan belajar menghitung, memvisualisasikan, menafsirkan, dan membandingkan kurva survival menggunakan model Kaplan-Meier, Weibull, dan Cox PH. Di akhir kursus, Anda akan mampu memodelkan distribusi survival, membuat plot kurva survival yang informatif, dan bahkan memprediksi durasi survival.

Persyaratan

Introduction to Regression with statsmodels in Python Hypothesis Testing in Python

1

Introduction to Survival Analysis

What problems does survival analysis solve, and what is censorship? You’ll answer these questions as you explore survival analysis data, build survival curves, and make basic estimations of survival time.

What is survival analysis?

What problems does survival analysis solve?

Choose the right data for survival analysis

Why use survival analysis?

Identify the censorship type

Preprocess censored data

First look at censored data

Your first survival curve!

Draw a survival curve

Long live democracy!

2

Survival Curve Estimation

In this chapter, you’ll learn how the Kaplan-Meier model works and how to fit, visualize, and interpret it. You’ll then apply this model to explore how categorical variables affect survival and learn how to supplement your analysis using hypothesis testing methods like the log-rank test.

Fitting a Kaplan-Meier estimator

How to fit a Kaplan-Meier estimator?

Heart disease patient survival

Visualizing your Kaplan-Meier model

Plotting the survival curve

Patient soreness treatment

Applying survival analysis to groups

Senators' terms in office

Comparing patient soreness treatments

The log-rank test

Appropriate data for log-rank test

Plotting and comparing survival curves

Log-rank test

3

The Weibull Model

Discover how to model time-to-event data with parametric models. Learn how to use the Weibull model and the Weibull AFT model and what different purposes they serve. Use survival regression to make inferences about how covariates affect the survival function and learn how to select the best survival model for your data.

Fitting the Weibull model

Model prison data with Weibull

Compare Weibull model parameters

Weibull model with covariates

Analyze heart patients characteristics

Explore gender-LVDD interaction

Visualization and prediction with Weibull model

How do prior arrests impact re-arrest rate?

Predict re-arrest rate

Other distributions and model selection

How good is the fit?

Choose a parametric model

4

The Cox PH Model

Another chapter, another model! In this final chapter, you'll learn about the proportional hazards assumption and the role it plays in fitting and interpreting the Cox Proportional Hazards model. You’ll also learn how to predict new subjects' survival times using the Cox Proportional Hazards model.

Fitting the Cox Proportional Hazards model

Model prison data with Cox PH

Custom Cox PH model

Interpreting the Cox PH model

Cox PH model survival time

Plot covariate effects on survival

The proportional hazards assumption

Test the PH assumption with KM curves

Test the PH assumption automatically

Predicting with the Cox PH model

Employee churn study

Predict before they leave!

Congratulations!

Analisis Survival dengan Python

Kursus
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