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!

Introduction to Survival Analysis

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.

Mencocokkan estimator Kaplan–Meier

Options

Needed

Not needed

Bagaimana melakukan fitting estimator Kaplan–Meier?

Kelangsungan hidup pasien penyakit jantung

Memvisualisasikan model Kaplan-Meier Anda

Memplot kurva survival

Perawatan nyeri otot pada pasien

Menerapkan analisis survival pada kelompok

Masa jabatan senator

Membandingkan perawatan nyeri pasien

Uji log-rank

Opsi

Data yang sesuai untuk uji log-rank

Memplot dan membandingkan kurva kelangsungan hidup

Survival Curve Estimation

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

The Weibull 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!

The Cox PH Model

Echocardiogram data

Employee attrition data

Regimes data

Prison recidivism data

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.

Introduction to Regression with statsmodels in Python

Hypothesis Testing in Python

Gunakan analisis survival untuk data waktu-ke-peristiwa dan prediksi waktu bertahan.

Analisis Survival dengan Python

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

Menerapkan analisis survival pada kelompok

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