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Logistic regression is very commonly used to analyse epidemiologic data from disease investigations where the disease outcome can be represented as a binary variable (0=no disease, 1=disease) and where multiple risk factors are being considered. Other types of analyses that may be considered include Poisson or negative binomial regression for count data (counts of the number of cases of disease), survival analysis for time to event (time to occurrence of disease) and possibly linear models when an outcome of interest is continuous (effect of disease status on body weight or growth).