In this kind of analysis you implicitly assume that the rates are constant over the period of the study, or as defined by the different groups you defined.īut, in longitudinal studies where you track samples or subjects from one time point (e.g., entry into a study, diagnosis, start of a treatment) until you observe some outcome event (e.g., death, onset of disease, relapse), it doesn’t make sense to assume the rates are constant. ![]() For example, we looked at how the diabetes rate differed between males and females. In the class on essential statistics we covered basic categorical data analysis – comparing proportions (risks, rates, etc) between different groups using a chi-square or fisher exact test, or logistic regression.
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