Missing data is a common problem in epidemiological studies and can lead to biased results if not handled properly. Methods for dealing with missing data include deletion, imputation, and model-based approaches. Each method has its advantages and disadvantages. For instance, deletion can lead to loss of valuable information, while imputation may introduce its biases. Researchers often disagree on the best approach to handle missing data, depending on the context of the study.
