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Epi Exam 2 > Measuring association and making causal inference > Flashcards

Measuring association and making causal inference Flashcards

1
Q

What is the epidemiologic approach?

A

Is there an association between a factor and the disease?

If yes, is it a causal relationship?

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2
Q

What are two ways to quantify the strength of an association (for a binary outcome)?

A
  1. difference

2. ratio

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3
Q

What is a binary outcome?

A

dependent variable we are looking at in a model
ill/well
case/control
alive/dead

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4
Q

If you had to translate the incidence ratio into words what would you say based on this example:
Tx 1 cumulative incidence = 0.5
Tx 2 cumulative incidence = 0.3
incidence ratio = (0.5/0.3) = 1.7

A

Treatment one increases the cumulative incidence of remission by 1.7 fold comparing to treatment 2

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5
Q

What type of difference measurements can be used for measures of association - binary outcomes?

A 
Risk difference (proportion)
Rate difference
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6
Q

What type of ratio measurements can be used for measures of association - binary outcomes?

A

Risk ratio (proportion)
Rate ratio
Odds ratio

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7
Q

What range do proportions, risks, cumulative incidence have to be in? Rates?

A 
Risk = 0-1
Rate = > 0
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8
Q

What does a difference of 0 imply?

A

no association

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9
Q

What do difference measures range from?

A
  • infinity to + infinity
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10
Q

What do ratio measures range from?

A

0 to infinity

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11
Q

What does a ratio of 1 imply? > 1? < 1?

A

1 – No association
> 1 – exposure is positively associated with disease
< 1 – exposure is negatively associated with disease

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12
Q

By looking at ratio, how will you decide which has the strongest association?

A

ratio further away from 1

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13
Q

How do you calculate a risk ratio?

A

risk proportion 1 / risk proportion 2

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14
Q

How do you calculate risk rate?

A

diseased / population at time

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15
Q

How do you find the risk?

A

diseased / population at risk

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16
Q

Define odds

A

the ratio of the probability an event will occur to the probability it will not occur

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17
Q

Is odds itself proportion, ratio, or probability?

A

ratio

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18
Q

What can odds range to if we calculate it?

A

0- infinity

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19
Q

What will the odds ratio tell us in words?

A

based on the conditions you are looking at factor x with increase the outcome odds by ___ fold compared to factor y

20
Q

Calculate the odds.

A

of dz animals/ # of non dz animals

21
Q

When thinking about causal association what 4 things could the association be due to?

A

Bias
Confounding
Chance
Cause

22
Q

What are the three main types of bias?

A
  1. Selection
  2. Information
  3. Specification
23
Q

T/F

Bias might be reduced or controlled in study design

A

True

24
Q

What else do we call bias?

A

systematic error

25
Q

What do you look at when deciding if something was associated by chance?

A

statistical significance.

26
Q

What is a selection bias?

A

The group used for a study is not a good representative of the population. They have been selected to fit certain criteria.

27
Q

What is an information bias?

A

The information in the study was inaccurately collected. The question was worded wrong, the recipient may have knowingly acted one way

28
Q

What is a specification bias?

A

Using a wrong statistical model that may make assumptions. It may make the wrong assumption

29
Q

What is confounding?

A

a lack of comparability between study groups resulting in a bias in the estimation of the effect of interest.

30
Q

What must a confounder be?

A
  1. cause of the outcome, independent the exposure
  2. correlated with exposure in the study population
  3. Not part of the causal pathway
31
Q

T/F

A confounder is affected by exposure or outcome

A

False

32
Q

What is chance also called?

A

random error

33
Q

What is a valid result?

A

This is an unbiased one. We are measuring exactly what we want to measure

34
Q

What is a precise result?

A

Has small variability

Use confidence interval to look at precision

35
Q

What are 4 major threats to a study’s validity?

A

selection bias
information bias
specification bias
confounding

36
Q

What are 3 factors that may affect precision of a study?

A 
Random variation (error)
Sample size
Study design (efficiency)
37
Q

What are Hill’s guidelines for causal inference?

A
  1. strength of association
  2. consistency
  3. specificity
  4. temporality (cause must occur first)
  5. biological gradient (dose-response)
  6. plausibility
  7. analogy
38
Q

What do Hill’s guidelines for causal inference help us do?

A

Helps us decide if a situation is more likely to be causal or not

39
Q

What is more likely to be causal, strong or weak association?

A

strong, however, a weak association can still be causal

40
Q

Why can’t we rule out causal association if there is poor consistency?

A

some effects are produced by their causes only under unusual circumstances.

41
Q

What does specificity suggest? Is this a good recommendation to assess causal relationship?

A

requires that a cause leads to a single effect, not multiple.
Not a good recommendation – most causes can lead to different effects

42
Q

What is a must for causal relationship according to Hill’s guidelines?

A

Temporality

refers to the necessity that the cause precedes the effect in time

43
Q

What is the analogy part of Hill’s guidelines?

A

Similar results can be found elsewhere

44
Q

T/F

One cause leads to one disease

A

False
Diseases have a multifactorial etiology – various factors interact to result in disease

Disease is an ecological problem

45
Q

What are the A-B-Cs?

A 
Is the observed Association due to:
Bias
Confounding
Chance
Cause

Epi Exam 2 (5 decks)

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