Question 1

What is the PH-assumption?

Accepted Answer

A)  The probability of Y=0 do not exceed 0.25 
B)  The hazard ratio increases over time 
C)  The hazard ratio decreases over time 
D)  The ratio of the hazard for subjects with different values on covariates is the same across time periods 
A)  The probability of Y=0 do not exceed 0.25 
B)  The hazard ratio increases over time 
C)  The hazard ratio decreases over time 
D)  The ratio of the hazard for subjects with different values on covariates is the same across time periods D

Question 2

What is it called when it is several possible different outcomes when moving from one state to another?

Accepted Answer

A)  Observed event times 
B)  Multiple events 
C)  Competing risks 
D)  Cox paradox 
A)  Observed event times 
B)  Multiple events 
C)  Competing risks 
D)  Cox paradox C

Question 3

In survival analysis, which technique do we use to deal with those subjects who do not experience the target event during the period of data collection?

Accepted Answer

A)  Regression 
B)  Listwise deletion 
C)  Multiple imputation 
D)  Censoring 
A)  Regression 
B)  Listwise deletion 
C)  Multiple imputation 
D)  Censoring D

Question 4

What information does the hazard function give us?

Accepted Answer

A)  It shows the conditional probability that a subject will experience the event in a given time period 
B)  It shows the probability that a subject will survive past a given time period 
C)  It shows the likelihood that a subject will not experience the event in a given time period 
D)  It shows the probability that a subject will survive the period of data collection 
A)  It shows the conditional probability that a subject will experience the event in a given time period 
B)  It shows the probability that a subject will survive past a given time period 
C)  It shows the likelihood that a subject will not experience the event in a given time period 
D)  It shows the probability that a subject will survive the period of data collection

Question 5

When is a proportional hazard model (Cox) used?

Accepted Answer

A)  When we wish to graph the survival function of our single event data 
B)  When we wish to investigate the effect of multiple covariates on the time for a certain event to take place 
C)  When we wish to test both time-invariant and time-varying covariates 
D)  When we wish to produce the cumulative survivor function 
A)  When we wish to graph the survival function of our single event data 
B)  When we wish to investigate the effect of multiple covariates on the time for a certain event to take place 
C)  When we wish to test both time-invariant and time-varying covariates 
D)  When we wish to produce the cumulative survivor function

What is the PH-assumption?

What is it called when it is several possible different outcomes when moving from one state to another?

In survival analysis, which technique do we use to deal with those subjects who do not experience the target event during the period of data collection?

What information does the hazard function give us?

When is a proportional hazard model (Cox) used?

Research and Statistics

Introduction to STATA

Simple Bivariate Regression

Multiple Regression

Dummy-Variable Regression

Interaction/Moderation Effects Using Regression

Linear Regression Assumptions and Diagnostics

Logistic Regression

Multilevel Analysis

Panel Data Analysis

Time-Series Analysis

Exploratory Factor Analysis

Structural Equation Modelling and Confirmatory Factor Analysis

Advanced Statistical Techniques

Programming and Dynamic Reporting Using STATA

Filters

Exam 9: Survival Analysis

What is the PH-assumption?

What is it called when it is several possible different outcomes when moving from one state to another?

In survival analysis, which technique do we use to deal with those subjects who do not experience the target event during the period of data collection?

What information does the hazard function give us?

When is a proportional hazard model (Cox) used?

Research and Statistics

Introduction to STATA

Simple Bivariate Regression

Multiple Regression

Dummy-Variable Regression

Interaction/Moderation Effects Using Regression

Linear Regression Assumptions and Diagnostics

Logistic Regression

Multilevel Analysis

Panel Data Analysis

Time-Series Analysis

Exploratory Factor Analysis

Structural Equation Modelling and Confirmatory Factor Analysis

Advanced Statistical Techniques

Programming and Dynamic Reporting Using STATA

Filters