I have the following data:
- Month-year number of people with back/neck problem
- number of people using surgery (lumbar fusion) as a treatment
The goal is to compute usage rate of surgery (
=number of surgeries / number of people with back/neck problem) for each month and hence come up with monthly forecast. What is the best approach to model this data?
I tried to figure out seasonality in the data, but with little success. Is it possible that my data does not have any seasonality? If I use Poisson regression, then do I have to test for normality? Or can I just ignore the distribution of the data. A step by step procedure is requested as I am new to this field.
I have data from January 2006 to May 2012. My counts are anywhere between 1 to 70. I am using sas and sas jmp. There is a limitation on the version of sas that I use. It has no time series license.
There is no seasonality in number with back/neck problem. So, I guess there will be no seasonality in number of surgeries either.
Since, I have count data I am using Poisson Regression to model number of surgeries. But Poisson regression has some strong assumptions. Another possible method is Negative Binomial Regression.
Currently my model contains a lagged dependent variable (lagged number of surgeries) and number with back/neck pain.
I hope I am on the right path. Or do you suggest a better approach?