Case-control study
Martin van der Esch, PhD
Amsterdam Rehabilitation Research Center | Reade
Case-control Study – Design
Select subjects on the basis of disease status
Disease
+
-
Exp +
a
b
Exp -
c
d
d1
d0
Amsterdam Rehabilitation Research Center | Reade
1
• Odds of exposure among cases = a / c
• Odds of exposure among controls = b / d
Disease
case control
Exp +
a
b
Exp -
c
d
d1
d0
Amsterdam Rehabilitation Research Center | Reade
2
RR  OR
Amsterdam Rehabilitation Research Center | Reade
Fletcher RH et al; Clinical epidemiology; LWW; 2014: pg 87
Analysis of CCS: The OR as a measure of
association
• The only valid measure of association for the CCS is the Odds
Ratio (OR)
• Under reasonable assumptions (– the rare disease assumption)
the OR approximates the RR.
• OR = Odds of exposure among cases (disease)
Odds of exposure among controls (non-dis)
• Odds of exposure among cases = a / c
• Odds of exposure among controls = b / d
• Odds ratio = a/c = axd [= cross-product ratio]
b/d bxc
Amsterdam Rehabilitation Research Center | Reade
4
Example CCS - Smoking and Myocardial
Infarction
Study: Desert island, population = 2,000 people, prevalence of smoking = 50%
[but this is unknown], identify all MI cases that occurred over last year (N=40),
obtain a random sample of N=40 controls (no MI). What is the association
between smoking and MI?
MI
+
-
Smk +
30
20
Smk -
10
20
40
40
OR = a . d = 30 . 20 = 3.0 (same as the RR!)
c.b
10 . 20
5
Amsterdam Rehabilitation Research Center | Reade
Odds Ratio (OR)
Similar interpretation as the Relative Risk
OR = 1.0 (implies equal odds of exposure - no effect)
ORs provide the exact same information as the RR if:
• controls represent the target population
• cases represent all cases
• rare disease assumption holds (or if case-control study is
undertaken with population-based sampling)
Remember:
•
•
•
OR can be calculated for any design but RR can only be
calculated in RCT and cohort studies
The OR is the only valid measure for CCS
Publications will occasionally mis-label OR as RR (or vice
versa)
Amsterdam Rehabilitation Research Center | Reade
6
Controlling extraneous variables
(confounding)
Exposure of interest may be confounded by a factor that
is associated with the exposure and the disease i.e., is
an independent risk factor for the disease
B
A
C
Amsterdam Rehabilitation Research Center | Reade
7
How to control for confounding
At the design phase
• Randomization
• Restriction
• Matching
At the analysis phase
• Age-adjustment
• Stratification
• Multivariable adjustment (logistic
regression modeling, Cox regression
modeling)
Amsterdam Rehabilitation Research Center | Reade
8
Matching is commonly used in CCS
Control an extraneous variable by matching controls to
cases on a factor you know is an important risk factor
or marker for disease
• Example:
• Age (within 5 years)
• Sex
• Neighbourhood
If factor is fixed to be the same in the cases and controls
then it can’t confound
Amsterdam Rehabilitation Research Center | Reade
9
Design of a case-control study
Fletcher RH. Clinical epidemiology 2014: pg 82
Amsterdam Rehabilitation Research Center | Reade
Case-control design
Identifying
basicpopulation
collecting
Measuring exposition
factor(s) and covariables
patients
effect?
Measuring exposition
factor(s) and covariables
controls
selecting
time
Amsterdam Rehabilitation Research Center | Reade
Steps to follow in
case-control study
Amsterdam Rehabilitation Research Center | Reade
Case-control study: steps to follow
1.
Selecting study population
1a. Selection patients
1b. Selection controls
2.
Measuring exposition factors (blinded)
- Central determinant
- Covariables (confounders and moderators)
3.
Analysis
- Unadjusted ‘Rough’ effect
- Adjusted effect (controlled for covariables)
Amsterdam Rehabilitation Research Center | Reade
1a. Selecting patients
SOURCE: ‘counters’ in healthcare
Hospital (department) Practice of physician
Depending of illness definition
screeningsprogram
INCIDENT or PREVALENT cases?
Prevalent cases
- Too many less severe cases
(especially in lethal diseases)
- Disease influences exposition-status
(especially in ‘variable’ determinants)
Incident cases
- Long ‘intake’-duration (especially in rare diseases)
- Severe diseases: ethical + practical objections
- Lethal diseases: † cases  † controles ?
- Clear beginning disease, diagnostics ?
Amsterdam Rehabilitation Research Center | Reade
Selection-bias
Information-bias
1b. Selection controles
•
Illness-free (or not having the disease of study)
Medical history? Assessments
•
Biomedical confirmation?
Source of origin
Theory
Study population
= Population of all persons who could have been at the
hospital, and could have been diagnosed with the disease of
study and having the disease
Private/primary pratice
Hospital
Open population
•
Inclusion- and en exclusion criteria
•
Matchen ?
Amsterdam Rehabilitation Research Center | Reade
Area
1b. Selection controles (continued)
SOURCE:
Hospital
(hospital-based
case-control study)
- Same mental state
- Comparable interview conditions
- High respons
No information-bias
- Which disease(s) ?
selection-bias
exclude etiological simularity !
Open population
(population-based
case-control study)
- Population register
- ‘random digit dialing’
matching
Area controles
- aselect, random sample
No selection-bias
- Different interview conditions
information-bias
- Bad respons, selective
Still selection-bias ?
- ‘overmatching’
Amsterdam Rehabilitation Research Center | Reade
1b. Selection controles (continued)
IN- en EXCLUSION criteria:
‘at risk’?
Historia morbi?
Same as for cases
Great danger: selection bias
MATCHEN:
Matching to reduce the chance of supposed confounders
Consequences for validity en efficiency?
Amsterdam Rehabilitation Research Center | Reade
2. Measurement of exposition-factors
1. Central determinant
2. Potential confounders / effectmodificators
Danger:
• Measurements incorrect / not comparable
• Measurements incomplete
Solution:
• ‘Blinded’ measurement
• Independent sample of participants
Problem:
• Timing of measurements (relevant timeframe?)
Amsterdam Rehabilitation Research Center | Reade
Information-bias
Analysis
Calculation of measurement of risk: Odds Ratio (OR)
(no incidence can be calculated:no RR calculation is possible)
1. Calculation of unadjusted risk
2. Calculation ‘adjusted’ risk
Stratified analysis
Multivariate models
Elimination of confounding
Demonstrating effectmodification
Amsterdam Rehabilitation Research Center | Reade
Advantages
and disadvantages
Amsterdam Rehabilitation Research Center | Reade
Advantages case-control design
•
Relative short duration
•
Relatively small sample size
(same precision of the risk estimation of the
effect parameter as in a cohort design)
Disadvantages case-control design
•
Validity under pressure
Comparability? Might be a problem, then….
not patients and controls are studied, but exposed and non-exposed
Selection-bias
Information-bias
Amsterdam Rehabilitation Research Center | Reade
Confounding
Cohort  Case Control
Amsterdam Rehabilitation Research Center | Reade
Fletcher RH et al; Clinical epidemiology; LWW; 2014: pg 81
COHORT vs CASE-CONTROL DESIGN
Cohort
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correct, ‘natural’ sequence
insight in risk actors
observations at level of persons
studying multiple outcomes
studying multiple determinants
completeness in follow-up
disease(s) with low incidence-rates
disease(s) with low preclinical stage,
duration of incubation, latency duration
sensitive for confounding
sensitive for information-bias
sensitive for information-bias
sensitive for selection-bias
size of study population
cost
duration
Amsterdam Rehabilitation Research Center | Reade
Case-control
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Summary
Amsterdam Rehabilitation Research Center | Reade
Summary
1
verzamelen
Measuring
exposition-factor(s)
+ other determinants
Cases
3
- exposed
- non-exposed
identification
Basic population
effect?
Measuring
exposition-factor(s)
+ other determinants
2
time
Amsterdam Rehabilitation Research Center | Reade
Controls
3
- exposed
- non-exposed
selecting
Questions?
Amsterdam Rehabilitation Research Center | Reade