1. the Cox proportional hazards model (Cox Regression Model)

2. Cox Regression
h(t), represents how the risk changes with time, and exp represents the effect of covariates (or factors/features)
identifying significant prognostic factors
most commonly used

3. Cox Regression
To estimate the coefficients, β, Cox (1972) proposes a partial likelihood function based on a conditional probability of failure
Statistical Methods for Survival Data Analysis, Third Edition, A JOHN WILEY & SONS, INC., PUBLICATION
By solving the regression problem, we get:
Regression Coefficient
(>0 bad, <0 good, =0 no effect)
Standard Error
p-Value

4. A Example If we consider standard Error Age (1.12, 6.75)
The estimated risk of dying for patients at least 50 years of age is exp(1.01)=2.75 times higher than that for patients younger than 50.
Patients with 100% cellularity have a 42% higher risk of dying than patients with less than 100% cellularity.
If we consider standard Error
Age (1.12, 6.75)
Cellularity (0.60, 3.35)

5. Thanks