Admissions per year before and after antibiotics? I need to compare hospital admissions per year before and after the introduction of antibiotics in 100 patients (antibiotics introduced at various time points in the past).
Eg.
Before antibiotics: admissions: 2   start: Jan-09   end: Dec-13   years: 4.92   admissions/year: 0.4
After antibiotics: admissions:0     start: Dec-13   end: June 19  years: 5.5    admissions/year: 0
If I use a t test then 0 admissions in 1 month equals to 0 admissions in 5 years, which is not correct.
Would you recommend any specific software?  
 A: I would start with Poisson regression, maybe with a random effect for patient. First, present the data in the following long format (you will need some transformations):
ID     Antibiotics   Admissions_total   Time_length ...
1      without         10                  3
1      with            8                   3
2      without         11                  4
2      with            3                   1
 ...

so you have to calculate total admissions, not per year, and length of period with/without in years/months. The length will then enter as an offset, that is, a variable with a known coefficient of 1. See Scaling vs Offsetting in Quasi-Poisson GLM.  The model can then be written as 
$$
   \text{Admissions-total}_i \sim \mathcal{Poisson}(e^{\lambda_i})
$$
where $\lambda_i= \mu + \tau_i + \text{Antibiotics}_i +\text{offset}(\log{\text{Time_length}})$. Here $\tau_i$ (if included in the model) is a patient random effect.
A simpler model without the random effect can be implemented in R with 
mod_glm <- glm(Admissions_total ~ Antibiotics + offset(log(Time_length)), family=poisson, data=your_data_frame)

but as there might be large differences between patients, it is probably better to include random effects, which in R could be
library(lme4)
mod_lme4  <- glmer(Admissions_total ~ Antibiotics + offset(log(Time_length)) +(1 | ID), data=your_data_frame, family=poisson)

This could at least be a starting point.  The model is called a Poisson rate regression.  
While I used R in my example, this could be done with most statistical software.
