I have a large set of data for 37 different clinical units (all oncology) in their respective 37 hospitals. There are two specific outcome variables that I need to analyse:
First, drug usage for specific drugs types and classes (aggregated drugs) that are expressed as a rate – DDD (Defined Daily Doses) per 100 patient days. There are individual patient drug use figures for this set.
Question1: Which regression approach should I take? From what I can gather I can use a Poisson regression model. IF there is overdispersion in the outcome I could resort to a negative binomial model.
Second: I have antibiotic resistance data that is expressed as proportion in the range 0 – 1.These are not available as individual patient data points but aggregated to each of the 37 hospitals.
Question 2: Again, which approach? From what I have read I can use a logistic regression model. I have been advised by another statistician to initiall use a logit model and then use a probit model and compare goodness of fit for each model.
Does this sound like a reasonable approach? Is there a specific text that you could direct me to in order to upgrade my basic regression modelling skills. I will be using R for the analysis.
Thanks in advance.