Which measure is commonly used in case-control studies and logistic regression to estimate association between exposure and outcome?

Which measure is commonly used in case-control studies and logistic regression to estimate association between exposure and outcome?

• A. Relative risk
• B. Odds ratio
• C. Risk difference
• D. Hazard ratio

Answer: (B)

In case-control studies and in logistic regression, the measure used to estimate how strongly exposure is associated with an outcome is the odds ratio. In case-control designs, you start with individuals based on outcome status, so you don’t have information to directly compute risk or incidence in the population. Instead, you compare the odds of having been exposed among cases to the odds of exposure among controls. That ratio of odds reflects the strength of the association between exposure and disease. In logistic regression, the model estimates how predictors affect the log odds of the outcome, and exponentiating the coefficients yields odds ratios for a one-unit change in the predictor, making odds ratios the natural effect size to report. When the outcome is rare, the odds ratio closely approximates the relative risk, which is why it’s often interpreted in a way similar to risk. The other measures aren’t as appropriate here: relative risk requires incidence data, hazard ratio comes from time-to-event analyses, and risk difference is a straight difference in risk rather than a ratio of odds.

In case-control studies and in logistic regression, the measure used to estimate how strongly exposure is associated with an outcome is the odds ratio. In case-control designs, you start with individuals based on outcome status, so you don’t have information to directly compute risk or incidence in the population. Instead, you compare the odds of having been exposed among cases to the odds of exposure among controls. That ratio of odds reflects the strength of the association between exposure and disease. In logistic regression, the model estimates how predictors affect the log odds of the outcome, and exponentiating the coefficients yields odds ratios for a one-unit change in the predictor, making odds ratios the natural effect size to report. When the outcome is rare, the odds ratio closely approximates the relative risk, which is why it’s often interpreted in a way similar to risk. The other measures aren’t as appropriate here: relative risk requires incidence data, hazard ratio comes from time-to-event analyses, and risk difference is a straight difference in risk rather than a ratio of odds.