Case-control designs are used to investigate rare outcomes and generate hypotheses
To conduct a case-control study, researchers choose a sample of people that have an outcome (cases) and then they choose a comparable sample of people that do not have the outcome (controls). The cases and controls are then compared on various risk factors associated with the outcome.
Case-control designs cannot generate measures of prevalence, nor can they infer causal effects, because of the lack of randomization. The researcher chooses how many people will be included in the study (leading to selection biases), so the prevalence in the entire population cannot be generated. There are also observation biases associated with using prior existing data that may or may not contain valid information.
Matching cases to controls adds extra complexity to case-control designs. It would be a tedious process to individually match each case to a comparable control. If at all possible, propensity score matching should be used to match cases to controls in the selection process. However, most times, researchers will compare the two groups on various demographic and prognostic factors to make sure that cases and controls are comparable at the group level.
Case-control studies can generate unadjusted odds ratios with 95% confidence intervals which provide a measure of strength of association between predictor and outcome variables. Between-subjects comparisons are often used with case-control designs.
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