Survival Analysis

1. Parametric

1.1. Linear Regression

1.1.1. Tobit

1.1.2. Buckley james

1.1.3. Panellzed Regression

1.1.3.1. Weighted Regression

1.1.3.2. Structured Regularization

1.2. Accelerated Failure Time

2. Machine Learning

2.1. Survival Trees

2.2. Bayesian Methods

2.2.1. Naive Bayes

2.2.2. Bayesian Network

2.3. Neural Network

2.4. Support Vector Machine

2.5. Ensemble

2.5.1. Random Survival Forest

2.5.2. Bagging Survival Trees

2.6. Advanced Machine Learning

2.6.1. Active Learning

2.6.2. Transfer Learning

2.6.3. Multi-Task learning

3. Exploratory Topics

3.1. Early Prediction

3.2. Data Transformation

3.2.1. Uncensoring

3.2.2. Calibration

3.3. Complex Events

3.3.1. Competing Risks

3.3.2. Recurrent Events

3.3.2.1. Joint Model

4. Non Parametric

4.1. Kaplan Meier

4.1.1. Restricted Mean

4.1.2. Landmark Analysis

4.2. Nelson-Aalen

4.3. Life-Table

5. Semi Parametric

5.1. Cox Regression

5.1.1. Basic CoxPh

5.1.2. Penalized Cox

5.1.2.1. Lasso-cox

5.1.2.2. ridge-cox

5.1.2.3. En-Cox

5.1.2.4. OSCAR-Cox

5.1.3. Time-Dependent Cox

5.1.4. Cox Boost