Survival Analyses Flashcards

1
Q

Survival Analyses

A

Model ‘TIME TO EVENT’ data
Directly incorporates time into the analysis to show level of decay over time

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2
Q

Survival Data are inherently censored meaning what?

A

Not fully observed, the time to an event is unknown

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3
Q

When is survival analysis useful?

A

When follow-up is either incomplete or variable

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4
Q

Examples of Censoring are what?

A
  1. Loss to follow-up
  2. Study withdrawal
  3. An event occurs outside of the study period
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5
Q

Left Censoring

A

Before the study begins

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6
Q

Right Censoring

A

After the study ends

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7
Q

Interval Censoring

A

Missing observations during the study period itself

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8
Q

What type of outcome is seen in Survival Analysis?

A

Binary/Dichotomous
Yes/No outcome

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9
Q

Do Linear and Logistic Regression include censoring?

A

NO

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10
Q

What is Hazard Ratio?

A

First derivative of survival function, captures instantaneous slope

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11
Q

What is Survival Function?

A

Kaplan Meier: what is reported in the RCT
1:1 Comparison

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12
Q

What is Hazard Function?

A

Instantaneous risk of event at a certain time
-Can be used when non-randomized aka controlling for a variable so the data will not be skewed

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13
Q

Survival Analysis overall can be used for what?

A
  1. Account for censoring
  2. Compare survival times between 2 or more groups
  3. Assess relationships between hazard ratios and several covariates
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14
Q

What can be involved in Survival Analyses?

A
  1. Descriptive Statistics
  2. Bivariate Statistics
  3. Multivariate Statistics
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15
Q

Descriptive Analysis includes what?

A
  1. Average Survival
  2. Average Hazard Rate
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16
Q

What are the two most common methods in estimating the Survivor Curve?

A
  1. Kaplan Meier KM
  2. Cox Proportional Hazards Regression (COX Regression)
17
Q

Kaplan Meier KM Method

A

The KM survival curve is a graphical method of summarizing the probability of survival over time estimated from a sample
GENERATES A STAIR STEP survival curve

18
Q

Inferring KM

A

Separate KM curves can be estimated so statistically significant differences between the groups can be calculated via LOG RANK TEST (type of chi-square)

19
Q

Log Rank Test

A

Used to compare two survival curves
If there is a significant difference, one group will be viewed as having significantly greater survival times

20
Q

What is the main limitation of the Log Rank Test?

A

UNIVARIABLE, meaning it does NOT account for confounding by other covariates or effect modification

21
Q

Is confounding by other covariates a problem in randomized clinical trials?

A

NO, randomization is intended to uniformly distribute covariates across groups

22
Q

What is the most common multivariable extension of the log-rank test?

A

COX Regression

23
Q

Does COX Regression produce a stair step survival curve?

24
Q

COX Regression is the MAIN method for RCT trials since it is the most common what?

A

Multivariable extension of the log rank test

25
COX Regression builds on what?
Builds upon the concept of a hazard
26
Hazard is defined as what?
The risk that a specific event will occur at any given time
27
Hazard Rate
Number of events that occur per interval of time
28
Hazard Function
Collection of an individual's hazard for an event over time
29
COX Regression mathematically what?
Separates the baseline hazard function from time-independent covariates
30
The KEY assumption for COX Regression is what?
Proportional Hazards, must test if the assumption holds via various statistical diagnostics
31
Proportional Hazards
Predictors have a constant proportional effect on the outcome -If the assumption is violated, it means a given predictor usually has a time dependent proportional effect
32
Strengths of COX and KM
1. A formalized multivariable approach to incorporate time-dependent censoring 2. COX regressions can incorporate several covariates 3. Hazard ratios are a type of relative risk measure
33
Hazard Ratio Meanings
1. Hazard Ratio <1 = increased predictor leads to decreased hazard 2. Hazard Ratio = 1 = no statistical difference 3. Hazard Ratio >1 = increased predictor leads to increased hazard
34
Limitations of COX and KM
1. Does not automatically accommodate predictor variables that change over time