  A common test for the diagnosis of type 2 diabetes is the Oral Glucose
Tolerance Test (OGTT). Recent developments in the study of OGTT tests have
framed it as a Bayesian inverse problem. These data analysis advances promise
great improvements in the descriptive power of OGTTs. OGTT tests are typically
done with invasive, bothersome, and somewhat expensive venous blood tests. A
natural question is whether improved data analysis techniques would allow for
less invasive and cheaper glucometer measurements to be used. In this paper we
explore this question. Using one dynamic model, we develop an error model for
glucometer capillary blood sugar measurements and compare results of venous
blood sugar tests for 65 patients, finding a match in over 90% of observed
cases. Our conclusion suggests that this model (or one much like it) may permit
capillary glucose to be used with reasonable accuracy in performing OGTTs.
