Dear all:

I’m new to this list – whose ideators and moderators I wish to sincerely thank for setting up and running – so apologies in advance if cross-posting.

I’m member of a team of statisticians and data managers carrying out comparative effectiveness evaluations of health-care transformation programmes in England. I’ve recently been tasked with deriving measures of absolute risk – notably, absolute risk differences, as described in e.g. Austin (JCE 2010, DOI: https://doi.org/10.1016/j.jclinepi.2008.11.004) – from a GLM Poisson log-link model of emergency hospital admissions fitted to an array of covariates including a binary exposure indicator.

While I think I got a decent understanding on how to go about such task (both conceptually and operationally) for a logistic regression model, I’m unconvinced absolute risk differences make sense from the above mentioned Poisson regression set-up, which models rate-type parameters and produces inferences on relative risks. Absolute risk measures seem to be only extracted from models of probability-type parameters (i.e. logistic regression or survival models); at the same time, researching Google for statistical publications reporting absolute risk measures from Poisson regression turned up nothing except (numerical and conceptual) warnings about GLM models with Binomial errors and log-links.

I should be grateful for any insight more experienced statisticians in the field than me may be able to offer.

With many thanks in advance for your time, all the best,

–

Stefano Conti