Lecture 2
Event Time Data
Censoring & Truncation
More Examples on Event History Analysis
Medical field
Death, Relapse, Occurrence of symptoms, disease onset
Reliability
Machine breakdown, Machin
Topic 4:
Assessment and Development of
the Cox Proportional Hazard Model
In the last chapter, we saw some of the mechanics of the Cox model. We
covered its derivation, how to fit it, how to compare ha
Topic 4-2: A further method for assessing the
PHM
4.5.1 Motivation for extending the PHM
The use of time-dependent covariates allows us to relax the proportional
hazards assumption as well as gives us
R-Implementation:
1. Read Data in R
2. #reads the file dataset.dat from the working directory:
> read.table("dataset.dat")
#reads the file dataset.dat from web page:
read.table("http:/./dataset.dat")
Topic 3-1: Coxs proportional hazards
model
(Time-independent Covariates)
1. The Proportional Hazards Model
2. Partial Likelihood Formulation
3. Treatments for Tied Event Times
300+ papers or books
adv
Lecture 4
Kaplan-Meier Estimate of S(t)
=
= ( > ) for continuous
Life Table
At the dawn of modern statistics, in the 17th century, John
Graunt and William Petty pioneered the study of mortality
The
Topic 2-3: Hypothesis Test
1. Log-Rank Test
2. Weighted Log-Rank Test
3. K-Sample Comparison
4. Trend Test
5. Stratified Log-Rank Test
1. Log-Rank Test
1|P a g e
In many biomedical researches, one is
Topic 3-2: Coxs proportional hazards
model
(Time-independent Covariates)
4. Inference for Regression Parameter
5. Estimation of Survival Function
1
4. Inference for Regression Parameter
Estimation of
Topic 5
Stratified and Extended Proportional Hazards Model
The proportional hazards model has the following advantages:
Nice interpretation
Theoretical properties have been studied extensively
Soft
Topic 1
Xiaoli Gao
1
Introduction
Survival analysis is a collection of various methods for analyzing the time
to a certain event, it is often a common practice in biomedical study
Time to death
Tim
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