Video summary
Case Control Study Explained: Concept, Design & Odds Ratio Numericals | Dr. Priyanka Sachdev
Main summary
Key takeaways
Main ideas / lessons conveyed
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Purpose of epidemiological studies
- Epidemiological studies are conducted to prove a hypothesis.
- A hypothesis typically links a cause/exposure/risk factor to an effect/disease (e.g., smoking → lung cancer).
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Types of epidemiological studies
- Observational studies
- Descriptive studies: formulate the hypothesis by describing time, place, person; do not measure association.
- Analytic studies: test the hypothesis by measuring association (focus of the session).
- Experimental studies
- Example: Randomized Controlled Trial (RCT), where the investigator intervenes.
- Observational studies
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Analytic studies: Case-control vs Cohort
- Case-control study
- Starts with disease/outcome status (cases vs controls) and looks back to past exposure.
- Retrospective / backward-looking
- Described as effect → cause.
- Cohort study
- Starts with exposure status (exposed vs unexposed) and follows people forward to see who develops disease.
- Prospective / forward-looking
- Cause → effect (outcome in the future).
- Case-control study
Case-control study: concept, design, and workflow
Key concept
- Called “case-control” because it compares:
- Cases = people with the disease present
- Controls = people with the disease absent
- The study then compares prior exposure (e.g., smoking history) between cases and controls.
Step-by-step methodology (as taught)
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Select the population
- Choose a defined population (e.g., Delhi/Mumbai/community).
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Select cases
- Case definition: a person in whom the disease under study is present.
- Must use diagnostic/eligibility criteria (e.g., lung cancer confirmed by biopsy/CT criteria as appropriate).
- Example: take 100 lung cancer patients from a cancer hospital.
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Select controls
- Control definition: a person in whom the disease under study is absent.
- Other diseases may exist, but the target disease (e.g., lung cancer) must be absent.
- Matching idea: make cases and controls similar in factors like age group and gender (and generally other confounders) except for disease status.
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Measure exposure (ask for history)
- Collect exposure history for each participant (e.g., smoking in the last 10–15 years):
- Exposure presence/absence (smokes vs does not smoke)
- Optionally intensity/frequency (e.g., cigarettes per day)
- Collect exposure history for each participant (e.g., smoking in the last 10–15 years):
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Create the 2×2 classification
- Divide into four groups:
- A: exposed + disease present
- B: exposed + disease absent
- C: unexposed + disease present
- D: unexposed + disease absent
- Divide into four groups:
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Decide retrospective design
- If you start at the present (disease already diagnosed) and go back using history, it is:
- Retrospective (backward-looking)
- Effect → cause
- If you start at the present (disease already diagnosed) and go back using history, it is:
Cohort study: design contrast (as taught)
Detailed design points
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Start point = exposure
- Take exposed group (e.g., smokers) and unexposed group (non-smokers).
- Do not intervene; observe naturally (as described in the lecture).
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Follow-up
- Follow for a long time (e.g., 10 years).
- Periodically check whether participants develop the disease.
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Therefore
- Prospective (forward-looking)
- Cause → effect
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Primary contrast in timing
- Case-control: start with disease → look back for exposure
- Cohort: start with exposure → observe disease in the future
Advantages & disadvantages (comparison provided)
Case-control study
- Advantages
- Fast (can be completed quickly; lecture implies “in one day” for concept level)
- Inexpensive/low cost
- No ethical issue (you’re not intervening; you’re asking history)
- Disadvantage
- Recall bias
- Exposure is based on participant memory/history, which may be inaccurate.
- Recall bias
Cohort study
- Advantages
- No recall bias
- Disease occurrence is observed during follow-up rather than relying solely on memory.
- No recall bias
- Disadvantages
- Time-consuming (requires long follow-up)
- Expensive (calls, visits, emails, etc.)
- Possible ethical concerns
- In smokers vs non-smokers, investigators may know risks but are not intervening in the described cohort setup.
Analysis in case-control: exposure rate and Odds Ratio (OR)
1) Build the 2×2 table (required rule)
- Disease categories across the top
- Disease present | Disease absent
- Exposure categories along the side
- Exposure present | Exposure absent
- Cell mapping:
- A (exposed + disease present)
- B (exposed + disease absent)
- C (unexposed + disease present)
- D (unexposed + disease absent)
2) Exposure rate (association present or absent)
- Exposure rate among cases = A / (A + C)
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Exposure rate among controls = B / (B + D)
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Decision rule
- If exposure rate among cases > exposure rate among controls → association present
- If exposure rate among cases < exposure rate among controls → association absent/negative
3) Odds Ratio (strength/direction of association)
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Odds Ratio formula (cross-product ratio):
- OR = (A × D) / (B × C)
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Interpretation
- OR = 1 → no association
- OR > 1 → positive association (higher odds of disease with exposure)
- OR < 1 → negative association / protective effect (lower odds of disease with exposure)
Numerical example (Odds Ratio) solved in the lecture
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Context: smoking effect on renal cell carcinoma
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Given
- 50 patients (disease present):
- 30 smokers → A = 30
- Total disease present A + C = 50 → C = 20
- 50 controls (disease absent):
- 10 smokers → B = 10
- Total controls B + D = 50 → D = 40
- 50 patients (disease present):
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Odds Ratio
- OR = (A×D)/(B×C) = (30×40)/(10×20) = 1200/200 = 6
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Result
- OR = 6 (strong positive association in this example)
Exam-focused MCQ conclusions included
- OR = 1 implies no association.
- Case-control is suitable for rare disease.
- Cohort is suitable for rare exposure.
- Recall bias is a key drawback of case-control (so “minimal bias” is not correct).
- Odds ratio (as cross-product) is tied to the case-control 2×2 setup.
Speakers / sources featured
- Dr. Priyanka Sachdev (speaker/instructor for the session)
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