Ways to account for the random sampling include conditional logistic regression and using inverse probability weighting to adjust for missing covariates among those who are not selected into the study.
They have pointed the way to a number of important discoveries and advances. However, because the difference between the cases and the controls will be smaller, this results in a lower power to detect an exposure effect.
Exposure is defined prior to disease development based on data collected at baseline or on assays conducted in biological samples collected at baseline. In retrospective studies the odds ratio provides an estimate of relative risk. The difference between sampling from the whole population and only the non-diseased is that the whole population contains people both with and without the disease of interest.
Analysis[ edit ] Case—control studies were initially analyzed by testing whether or not there were significant differences between the proportion of exposed subjects among cases and controls.
Obviously, this is a much more efficient design. The results may be confounded by other factors, to the extent of giving the opposite answer to better studies.
In essence, a case-control strategy was used, but it was conducted within the context of a prospective cohort study. With a case-control sampling strategy one simply takes a sample of the population in order to obtain an estimate of the exposure distribution within the population that gave rise to the cases.
Date last modified: June 7,