Case control studies

Question 1

Where in the hierachy of evidence does a case-control study lie?

Answer 1

3rd

Question 2

What is a case control study?

Answer 2

Recruit as cases or control (based on whether they have the disease or not) and then look back on their lives

= know outcome when they are recruited

Question 3

How are people recruited for a case control study?

Answer 3

1. Identify cases (people with outcome)
2. Obtain information about exposure
3. Identify controls (people without outcome)
4. Obtain information about exposure

Question 4

When is frequency of exposure measured?

Answer 4

Retrospectively

Question 5

What does the way a case is defined depend on?

Answer 5

the outcome of interest e.g. pathological, radiological, microbiological, self report

Question 6

How are controls defined?

Answer 6

Must be individuals who would have been cases if had developed outcome (represent population from which the cases came from)

Question 7

What are the possible sources of controls?

Answer 7

1. Other disease controls (have other disease not related to outcome) e.g. Hospital controls & cancer registry controls
2. Community based controls = PREFERABLE (healthy people) e.g. Electoral register, random digit dialling, primary care controls, spouse or family controls

Question 8

What are the advantages of using other disease controls?

Answer 8

Easy to locate subjects

Subjects more likely to participate in studies = cheaper

Question 9

What are the disadvantages of using other disease controls?

Answer 9

Hospital populations may be more likely than the general population to have the exposure of interest

Question 10

What are the advantages of using community-based controls?

Answer 10

Prevalence of exposure not likely to be due to other disease

Question 11

What are the disadvantages of using community-based controls?

Answer 11

More difficult to locate and less likely to participate (more expensive)

Question 12

Which ratio of controls to cases should be used?

Answer 12

• At least 1 control per case
• Up to 4 controls per case if few cases (to improve power of study)
• Negligible benefit of more than 4 controls per case (not worth the expense)

Question 13

How is exposure measured in case control studies?

Answer 13

Retrospectively

Reported by subject or from records

(similar to historical cohort study)

Question 14

What are the differences between case control studies and historical cohort studies?

Answer 14

Case control study:

Recruit cases and controls over short defined time period

Can not calculate incidence rates/risk ratios (as total number in population usually unknown)

Historical cohort:

All those with outcome are included even if died years before study if exposure data still availiable

Can calculate incidene rates/risl ratios

Question 15

What is odds of outcome?

Answer 15

= odds ratio

The equivilent of risk ratio (which cannot be calculated from the date we have)

n. b. for rare outcome odds _~ risk
no. without outcome is approx. number in study

Question 16

How do we calculate odds?

Answer 16

Odds = no. with outcome/ no. without outcome

Question 17

What is an odds ratio?

Answer 17

Odds ratio = odds in exposed group/odds in unexposed group

n.b. the odds in each group are calculated seperately

Question 18

Which two test statistics can be calculated for an odds ratio?

Answer 18

P value (strength of evidence against null hypothesis - i.e. no association between exposure and outcome… OR = 1)

CI intervals (precision of estimated OR)

Question 19

Why do we use an odds ratio instead of a risk ration in case control studies?

Answer 19

In case controls studies:

• Cannot calculate risk of outcome or risk ratio
• Can calculate odds ratio (OR_exposure= OR_outcome)
• Rare outcomes (<10%) odds ratio is approximately equal to risk ratio

Question 20

How do you interpret an odds ratio <1?

Answer 20

Exposure reduces risk

Question 21

How do you interpret an odds ratio >1 ?

Answer 21

Exposure increases risk

Question 22

How do you interpret a RR > 1 when the outcome is not rare?

Answer 22

OR > RR

Question 23

How do you interpret a RR < 1 when the outcome is not rare?

Answer 23

OR < RR

Question 24

Would a case-control study require more or less subject than a cohort study?

Answer 24

Less = more powerful

e.g. case control based on 350 people nearly as powerful (precise) as cohort study based on > 20,000 people

Question 25

What are the strengths of case-control studies (4)?

Answer 25

• Relatively quick (no need to wait for incident cases)
• Efficient for rare outcomes (large smaples sizes and long follow ups NOT required)
• Allows examination of >1 exposure for 1 outcome
• No loss to follow up bias

Question 26

What are the weaknesses of case control studies (5)?

Answer 26

• Can not be sure exposure came before outcome = reverse causality (less likely when cases are a new diagnosis rather than prevalence of disease)
• Inefficient for rare exposures
• Can not calculate incidence rates
• Selection bias (cases and controls must be selected irrespective of their exposure)
• Measurement bias (recall = those with outcome more likely to think about exposure/ think they have when they haven’t & interviewer = may investigate differently for cases and controls)

Question 27

What is the null value for an odds ratio?

Answer 27

1 = no association

Question 28

In general what does an odds ratio assess?

Answer 28

The association between exposure and outcome (binary)

Question 29

When is the odds ratio likely to be a good approximation of the risk ratio?

Answer 29

When the outcomes are rare

Question 30

If you have an OR of 5.44 what does this show?

Answer 30

The odds (approximate risk) of those exposed getting the outcome are more than 5 times higher than in those who were not exposed

Question 31

What are the possible explanations for the association (other than causality)?

Answer 31

• Chance (v. small p value = unlikely)
• Bias (selection -> must be selected irrespective of their exposure; measurement = recall & interiew; loss to follow up unlikely)
• Confounding (e.g. age, gender, social class)
• Reverse causality (may change after diagnosis … BUT… lifetime exposure i.e. lifetime drinking not more recent increase in drinking in the more recent years due to diagnosis)