Subgroup analysis
Subgroup Analysis is a type of statistical analysis that is used in clinical trials and meta-analysis. It involves separating the data into subgroups and then analyzing these subgroups separately. This can be useful for identifying specific effects that may not be apparent when looking at the overall data.
Purpose[edit | edit source]
The main purpose of subgroup analysis is to identify specific effects in different subgroups. This can be useful for identifying potential treatment effects that may not be apparent when looking at the overall data. For example, a treatment may be more effective in one subgroup than in another, or it may have different side effects in different subgroups.
Methodology[edit | edit source]
Subgroup analysis involves separating the data into subgroups based on certain characteristics. These characteristics can include things like age, sex, race, or any other factor that may potentially affect the outcome of the study. Once the data has been separated into subgroups, these subgroups are then analyzed separately.
Limitations[edit | edit source]
While subgroup analysis can be a useful tool, it also has several limitations. One of the main limitations is that it can increase the risk of Type I error, which is the probability of incorrectly rejecting the null hypothesis. This is because the more tests that are performed, the greater the chance of finding a significant result by chance alone.
Another limitation is that subgroup analysis can lead to overinterpretation of the data. This can occur when researchers focus too much on the results of the subgroup analysis and ignore the results of the overall analysis.
See also[edit | edit source]
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