7 Case Control Study by n9P0aK


									Case-control study

Chihaya Koriyama
August 17 (Lecture 1)
   Study design in epidemiology


      individual            population

Case-control       Cohort   Ecological
   study           study       study
       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.
   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
  – 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.
     Various types of case-control studies

1)a population-based case-control study
      Both cases and controls are recruited from the

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.
  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.
 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
 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”.
                Error & Bias
• Error: random error

• Bias:systematic error
  – differential misclassification
           This is a problem!
  – non-differential misclassification
       An example of non-differential
  misclassification in an exposure variable
 We want to compare mean of blood
  pressure levels between cases and
 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
                                         Observed risk
  An example of non-differential        estimate always
  misclassification in the              comes close to
  ascertainment of exposure                 “1(null)”
                          Case    Control Odds ratio
 True (nobody   Exp +     1 50 10 9 50 10
                                          (50*90) /
                 Exp -      10      90    (50*10) =9
 Results of     Exp +        41        49     (41*91) /
 test*           Exp -       19        91     (49*19)=4.01

*Sensitivity 80% (80% of the exposed subjects are correctly
 Specificity 90% (90% of the un-exposed subjects are
correctly diagnosed)
        Differential misclassification

• Selection bias

• Detection bias

• Information bias
   – Recall bias
   – Family information bias

 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.
 Example of confounder
 - living in a HBRA is a confounder -
              HBRA: high background radiation area

                          Low socio-economical
                           A surrogate marker of low
   High infant death
                          status in HBRA
                           socio-economic status

Causation ?                  Living in a

    Exposure to
    radiation in uterus
  Example of confounder
  - smoking is a confounder -

       Myocardial             Smoking is a risk factor of MI

Causation ?                           smoking
(We observe an association)

                              related by chance
     Example of “not” confounder
     - pineal hormone is not a confounder -
                            EMF: electro-magnetic field

                                Decrease of pineal hormone
          Breast cancer         may be the risk of breast ca.

                                  Down regulation
 Causation ?                      of pineal hormone

               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.
  Why do we have to consider

We want to know the “real”
 causal association but a
 distorted relationship
 remains if you do not adjust
 for the effects of
 confounding factors.
    How can we solve the problem of

“Prevention” at study design
  Randomization in an
    intervention study
  Matching in a cohort study But
    not in a case-control study
     How can we solve the problem of

“Treatment “ at statistical analysis

  Stratification by a confounder
  Multivariate analysis
  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
     Incident or Prevalent cases with
     chronic disease(s)
Incident case              Prevalent case
• You recruit cases        • You recruit cases cross-
  prospectively.             sectionaly.

• Newly diagnosed cases    • Mixed cases with
                             diagnosed recently and
• All cases are alive.       long time ago.
                           • You miss patients who
                             died before study.
                              – Only survivors

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

• Matched by confounding
  – Sex, age ・・・・
• Cannot control confounding
  – Conditional logistic analysis is
• To increase the efficiency of
  statistical analysis
 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
           a case-control study

Cases         Controls   The incubation period
(brain tumor)            of tumor is a few years
                         at least.
N=100         N=100
Mobile phone users (NOT recently started)
↓              ↓
50             10
              Cases    Controls
     Yes       50        10
     No        50         90
 Risk measure in a case-control study
Odds = prevalence / (1- prevalence)
Odds ratio = odds in cases / odds in controls
                +(case)    -(control)
           +    a          c
Exposure -      b          d

Exposure odds in cases =a / b
Exposure odds in controls=c / d
Odds ratio=(a / b) / (c / d) = a * d / b * c
      Comparison of the study design

                 Case-control          Cohort

Rare diseases       suitable         not suitable
Number of disease       1                 1<
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

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