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Flashcards in Survival Analysis Deck (24)
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1

Survival Time

-Time to an event
-The time starting from a defined point to the occurrence of a given event

2

Events for the end of survival time include

death
disease occurrence
disease recurrence
recovery
other experience of interest

3

Special features in survival time data

-rarely normally distributed
-often skewed
-typically with many early events and relatively few late ones

4

Censored Observations

Those who have not yet reached the terminal event by the end of the study.

5

In censored observations, why might information about a pt's survival time be incomplete?

-pt hasn't experienced event by end of study
-pt lost to follow up during study period
-pt experiences different event to make further follow up impossible

6

Issues with censored observations in analysis

these censored survival times will underestimate the true (but unknown) time to the event because it will occur beyond the end of the study.

7

Kaplan-Meier Curve

-visualize estimate of survival over time

-shows probability of an event at a certain time interval

- x-axis for time, y-axis for 'proportion surviving'

-step function, as the cumulative survival remains the same until the day another person experiences the event

8

Kaplan-Meier Curve Censored Data

x-axis: time
y-axis: 'proportion surviving'
-step function
-censored observations indicated on K-M curve as "tick marks"
-censored observations do not terminate the interval

9

Kaplan-Meier Curve for 2 Groups

Visualizes the difference between two survival curves. Can be used to compare treatments.

10

Median Survival Time

estimated as small survival time for which survival function is less than or equal to 0.5

11

How can you estimate the Median Survival Time?

1. find the 50% mark on the proportion axis

2. drawing a horizontal line at 50% to find the crossing point with the K-M curve

3. drawing a vertical line at the crossing point down to the time axis to read time

12

Mean Survival Time

-area under survival curve
-may not be best estimate for sample of survival times, highly skewed
-median typically better measure of central location than mean

13

Hazard Rate

-measure of how often an event happens in one group compared to another

-in clinical trials, measures survival point at any point in time in a group given treatment vs control

-can be estimated as being a slope of a K-M curve

14

Interpreting Hazard Ratio

HR = 1

the event rates are the same in both groups

15

Interpreting Hazard Ratio

HR >1

the event rate in the treatment group is faster than in the control group

16

Interpreting Hazard Ratio

HR < 1

event rate in treatment group is slower than in the control group

17

Log-Rank Test

Compares two or more samples with survival data in presence of censored observations

18

How can a log-rank test fail

if two curves cross (no statistical power)

19

Assumption of Log-Rank Test

Hazard Rates of the groups to compare must be proportional

20

Limitations of Log-Rank Test

-We can only test one variable at a time

-can't control for potential confounders

-can't control for other potential risk factors

-can't include interaction terms

21

Cox Proportional Hazard Model

Most commonly used method comparing two or more samples with survival data in presence of censored observations

22

T/F: Cox model can accommodate only one confounding variable

False
can accommodate any number of confounding variables

23

T/F: Cox Model provides the estimate of Hazard Rate with its associated 95% Confidence Interval

True

24

Assumption of Cox Model

The ratio of the hazard functions for any two observations does not vary with time.