Case-Control 101 - Hindsight Detectives
- A retrospective study design that starts with an outcome (disease) and looks backward in time to find the exposure.
- Compares the odds of prior exposure between the two groups.
- Key metric: Odds Ratio (OR), calculated as $OR = (ad)/(bc)$.
⭐ Highly susceptible to recall bias, as cases (diseased) may remember past exposures differently than controls (healthy).

📌 Case-Control starts with the Consequence (disease) to find the Cause.
Odds Ratio - The Betting Statistic
- Represents the odds that a case was exposed compared to the odds that a control was exposed.
- Calculated from a 2x2 table: $OR = (a/c) / (b/d) = ad/bc$.
- a: cases exposed
- b: controls exposed
- c: cases unexposed
- d: controls unexposed
- Interpretation:
- OR > 1: ↑ Odds of exposure in cases (risk factor).
- OR = 1: No association.
- OR < 1: ↓ Odds of exposure in cases (protective factor).
⭐ In rare diseases (low prevalence), the Odds Ratio approximates the Relative Risk (RR).

Pros & Cons - The Give and Take
| Pros (Advantages) | Cons (Disadvantages) |
|---|---|
| * Quick & Inexpensive: Less time and resources needed compared to cohort studies. | * Recall Bias: Cases may remember exposures differently than controls. |
| * Rare Diseases: Excellent for studying diseases with low prevalence. | * Selection Bias: Controls may not be representative of the exposure in the population. |
| * Multiple Exposures: Can investigate many potential risk factors at once. | * Temporality Issues: Cannot definitively prove exposure preceded the disease. |
| * Fewer Subjects: Smaller sample sizes are typically required. | * Cannot Calculate Incidence/Prevalence: Only provides an odds ratio. |
Common Biases - The Usual Suspects
- Selection Bias: Controls are not representative of the population that produced the cases.
- Berkson's bias: Hospital-based controls differ from the general population.
- Neyman bias (prevalence-incidence): Excludes fatal or short-lived cases.
- Recall Bias: Cases may remember past exposures more accurately or vividly than controls.
⭐ Matching cases to controls on potential confounders (e.g., age, sex) is a key strategy to increase study validity.
High‑Yield Points - ⚡ Biggest Takeaways
- A retrospective design, looking back in time from disease to exposure.
- Excellent for studying rare diseases or those with long latency periods.
- Compares a group with the disease (cases) to a similar group without it (controls).
- The primary measure of association is the odds ratio (OR).
- Cannot be used to calculate prevalence or incidence.
- Major weaknesses include recall bias and selection bias.
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