I have a binary variable indicating whether a c-section was carried out or not. C-section is more than 10% prevalent in the population. I want to model predictors of c-section. Initially I had used an xtlogit
model in Stata to model this. The xtlogit command runs a multilevel model. I need a multilevel model because the outcome may be clustered at the hospital level, so I use a random intercept to allow for this. However, the odds ratios that are returned seem problematic. In some instances, an OR of >500 is given back for some explanatory variables. This is probably because the OR is overestimating the true underlying risk. This happens when a violation of the "rare outcome" assumption occurs (because c-section is >10% in the population - the assumptionis violated here). http://www.bmj.com/content/348/bmj.f7450.full?ijkey=NHT1YVsoX1RCm8r&keytype=ref
Instead, I ran an xtpoisson
model which gives risk estimates that are much more plausible. I used a poisson model because I read on this post that it is appropriate model binary outcomes in this manner when a RR is required. Poisson regression to estimate relative risk for binary outcomes
However, the estimates of variance are now completely different to what was estimated with the xtlogit
. For example in the xtlogit
model I calculated a VPC of 30%. This is now 2% from the xtpoisson
. Should the VPC have changed this much? Which is the more reliable estimate to use?