Case-control study

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Case-control study

• Chihaya Koriyama
• August 17 (Lecture 1)

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Study design in epidemiology
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Why case-control study?

• In a cohort study, you need a large number of the
  subjects to obtain a sufficient number of case,
  especially if you are interested in a rare
  disease.
• Gastric cancer incidence in Japanese male
• 128.5 / 100,000 person year
• A case-control study is more efficient in terms
  of study operation, time, and cost.

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Case-control study - subjects

• Start with identifying the cases of your research
  interest.
• If you can identify the cases systematically,
  such as a cancer registration, that would be
  better.
• Incident cases (newly diagnosed cases) are better
  than prevalent cases (survivors).
• Recruitment of appropriate controls
• From residents, patients with other disease(s),
  cohort members who do not develop the disease yet.

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Various types of case-control studies
1)a population-based case-control study Both
cases and controls are recruited from the
population. 2)a case-control study nested in a
cohort Both case and controls are members of the
cohort. 3)a hospital-based case-control
study Both case and controls are patients who
are hospitalized or outpatients.
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Who will be controls?

• Control ? non-case
• Controls are also at risk of the disease in
  his(her) future.
• In a case-control study of gastric cancer, a
  person who has received the gastrectomy cannot be
  a control.
• In a case-control study of car accident, a person
  who does not drive a car cannot be a control.

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Case-control study - information

• Collection of the information (past information)
  by interview, biomarkers, or medical records
• Exposure (your main interest)
• Potential confounding factors
• Bias Confounding
• Selection bias
• Information bias (recall bias)
• confounding

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Selection bias

• Sampling is required in a case-control study
  (since we cannot examine all!)
• We need to chose appropriate subjects.

Selection bias is Selection of cases and
controls in a way that is related to exposure
leads to distortions of exposure prevalence.
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Error Bias

• Error random error
• Biassystematic error
• differential misclassification
• non-differential misclassification

This is a problem!
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An example of non-differential misclassification
in an exposure variable

• We want to compare mean of blood pressure levels
  between cases and controls.
• The blood pressure checker has a problem and
  always gives 5mmHg-higher than true values.
• All subjects were examined by the same blood
  pressure checker.
• ? no problem for internal comparison

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An example of non-differential misclassification
in the ascertainment of exposure
Observed risk estimate always comes close to
1(null)
Case Control Control Odds ratio
True (nobody knows) Exp 50 50 50
True (nobody knows) Exp - 10 10 90
Results of test Exp 41 41 49
Results of test Exp - 19 19 91
10
10
1
9
(5090) / (5010) 9
(4191) / (4919)4.01
Sensitivity 80 (80 of the exposed subjects are
correctly diagnosed) Specificity 90 (90 of
the un-exposed subjects are correctly diagnosed)
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Differential misclassification

• Selection bias
• Detection bias
• Information bias
• Recall bias
• Family information bias

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Confounding

• Confounders are risk factors for the outcome.
• Confounders are related to exposure of your
  interest.
• Confounders are NOT in the process of causal
  relationship between the exposure and the outcome
  of your interest.

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Example of confounder- living in a HBRA is a
confounder -
HBRA high background radiation area
Low socio-economical status in HBRA
A surrogate marker of low socio-economic status
High infant death
Living in a HBRA
Causation ?
Exposure to radiation in uterus
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Example of confounder- smoking is a confounder -
Smoking is a risk factor of MI
Myocardial infarction
smoking
Causation ? (We observe an association)
Radiation
related by chance
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Example of not confounder- pineal hormone is
not a confounder -
EMF electro-magnetic field
Decrease of pineal hormone may be the risk of
breast ca.
Breast cancer
Down regulation of pineal hormone
Causation ?
EMF
EMF exposure induces down regulation of pineal
hormone
If EMF exposure cause breast cancer only through
down regulation of pineal hormone, this is not a
confounder.
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Why do we have to consider confounding?

• We want to know the real causal association but
  a distorted relationship remains if you do not
  adjust for the effects of confounding factors.

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How can we solve the problem of confounding?

• Prevention at study design
• Limitation
• Randomization in an intervention study
• Matching in a cohort study But not in a
  case-control study

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How can we solve the problem of confounding?

• Treatment at statistical analysis
• Stratification by a confounder
• Multivariate analysis

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Case ascertainment

• Who is your case?
• Patient?
• Deceased person?
• What is the definition of the case?
• Cancer (clinically? Pathologically?)
• Virus carriers (Asymptomatic patients)
• ? You need to screen the antibody

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Incident or Prevalent cases with chronic
disease(s)

• Incident case

• Prevalent case

• You recruit cases prospectively.
• Newly diagnosed cases
• All cases are alive.

• You recruit cases cross-sectionaly.
• Mixed cases with diagnosed recently and long time
  ago.
• You miss patients who died before study.
• Only survivors

Cases with better prognosis!
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Matching in a case-control study

• Matched by confounding factor(s)
• Sex, age
• Cannot control confounding
• Conditional logistic analysis is required.
• To increase the efficiency of statistical analysis

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Over matching

• Matched by factor(s) strongly related to the
  exposure which is your main interest
• CANNOT see the difference in the exposure status
  between cases and controls

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a case-control study
Cases Controls (brain tumor) N100 N100 Mobile
phone users (NOT recently started) ? ?
50 10 
The incubation period of tumor is a few years at
least.
Cases Controls
Yes 50 10
No 50 90
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Risk measure in a case-control study

• Odds prevalence / (1- prevalence)
• Odds ratio odds in cases / odds in controls
• Disease
• (case) -(control)
• a c
• Exposure - b d
• Exposure odds in cases a / b
• Exposure odds in controlsc / d
• Odds ratio(a / b) / (c / d) a d / b c

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Comparison of the study design
Case-control Cohort Rare diseases
suitable not suitable Number of disease 1
1lt Sample size relatively small need to
be large Control selection difficult
easier Study period relatively short
long Recall bias yes no Risk
difference no available available