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PASS > Case-control studies > Flashcards

Case-control studies Flashcards

1
Q

Concept of case control studies

A

Find cases
Find controls
Look at past exposures - RETROSPECTIVE
Compare exposure levels

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2
Q

What can we measure with case control study?

A

Odds ratio

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3
Q

Problem with odds ratio

A

It’s not a rate ratio - doesn’t show increased risk

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4
Q

error factor calculation

A

e to the power of 2x square root of 1/a + 1/b +1/c + 1/b

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5
Q

Calculating odds ratio

A

AD/BC

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6
Q

Why case control study vs cohort?

A

Cohort takes long time
Cohort is expensive
Cohort is not good for studying rare diseases

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7
Q

Confidence interval =

A

Odds ratio / error factor (lower value)

Odds ratio x error factor (upper value)

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8
Q

What does confidence interval tell us?

A

If null value (or 1 if ratio) is within interval then there is no statistical significance of data
BUT this does not show less risk (NOT A RATE RATIO)

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9
Q

What is the precision of odds ratio affected by?

A

Number of healthy people and cases

Its worth increasing controls up to a point (4-6x cases)

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10
Q

Case control vs cohort

A

Case control looks backwards (retrospective)

Cohort looks forwards (prospective)

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11
Q

Different types of cohort study?

A

Concurrent - prospective waiting for events to happen

Historical cohort - retrospective collection of data (eg from pre existing records)

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12
Q

Different types of case control study

A

Conventional - retrospective collection of data (from recall)

Nested case control study - collection of data from concurrent cohort study

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13
Q

What is a nested case control study?

A

Case control study is nested within a cohort study

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14
Q

Advantages of nested case control study over normal case control

A

AIncidence rates can be calculated

population for sampling of controls is defined

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15
Q

Advantages of nested case control over cohort study

A

Can collect more detailed information for minority of participants (eg blood samples or additional info)

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16
Q

Key issues case control study

A

Selection Bias
Information bias - eg recall bias
Confounding

17
Q

How to deal with selection bias?

A

Cases need to be representative of all cases

Controls need to be representative of population from where cases came from

18
Q

Information bias types

A

Non differentiated misclassification - randomly inaccurate measurement

Systematic (differentiated) misclassification - eg recall bias, assessor bias, different data collection methods

19
Q

Effect of non differentiated misclassification information bias on results

A

Observed odds ratio moves towards 1

“shrinkage to null”

20
Q

Effects of systematic (differentiated) information bias

A

Odds ratio can be further away OR towards null value of 1

21
Q

How can confounding be minimised?

A

Matching confounders in controls and cases

Adjusted for by analysing with logistic regression

22
Q

Cohort vs case control

A

Cohort:
Compare on outcome status
Expensive - takes long time
Can calculate rate ratio and odds ratio (incidence)
Not good for rare diseases
Study range of diseases/outcomes
Limits bias but prone to losses to follow up

Case control:
Compare based on exposure
Quicker - cheaper
Can only calculate odds ratio
Study range of exposures
Prone to selection and information bias
23
Q

Issues for cross sectional studies

A

Sampling bias
Responder/participant bias
Chance - random error

24
Q

analysis case control vs cohort

A

Cohort: odds ratio and rate ratio

Case control: just odds ratio

25
Q

Studies prone to sampling bias

A

All (ecological, cross sectional, case control and cohort)

26
Q

Studies prone to selection bias

A

Case control (highest) and cohort

27
Q

Studies prone to confounding

A

All (ecological, cross sectional, case control and cohort)

28
Q

Studies prone to recall bias

A

Case control, cross sectional survey, cohort

29
Q

Studies prone to random error

A

ALL

PASS (25 decks)

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