02-19-2018 12:28 PM
I am using proc glimmix (SAS 9.4) for a two level model (health care providers and organizations, which are both categorical) that regresses provider type (0/1) on imaging (0/1). health care provider is specified as the random intercept.
There are 92,100 cases nested in 7,133 providers, nested in 2,370 organizations. All providers and organizations have at least 5 cases and there are no multi-organization providers.
The model results are consistent with what I would expect, until I add organization as a fixed effect. The fixed effect intercept increases considerably, which triples the predicted probability (model 3). The syntax and models are below. Any ideas as to what the problem could be?
PROC GLIMMIX DATA=please_work METHOD=LAPLACE NOCLPRINT;
CLASS provider org;
MODEL X_RAY (EVENT=LAST) = provider_type org / CL DIST=BINARY LINK=LOGIT SOLUTION ODDSRATIO (DIFF=FIRST LABEL);
random intercept / type=vc subject=provider;
COVTEST / WALD;
|Model 1||model 1b||model 1c||Model 2||Model 3|
|changes to model||naïve, provider as class, random effects for the provider intercept||naïve, organization as class, random effects for the organization intercept||org and provider as class, org as fixed effect, random provider intercept||Model 1A + added provider type to fixed effects||model 2 + org as class and fixed effect|
|Fixed Effects (Solution for Fixed Effects)|
|Intercept (log odds of receiving a xray with a typical provider)||-1.204||-1.2581||0.9022||-1.23||0.5816|
|Error Variance ( Covariance Parameter Estimates)|
|(<.0001)||(<.0001)||(<.0001)||(<.0001)||(std error 0)|
|(-2) Log Likelihood (lower is better)||100,237.00||100,847.70||95,864.76||100,135.00||95,729.98|
02-19-2018 01:38 PM - edited 02-19-2018 02:16 PM
Adding a class variable into a model will change the intercept. This is a known fact, based upon the way SAS handles categorical variables. I don't see anything wrong here.
Predicted probability of what?