Conditional categorical response models with application to treatment of acute myocardial infarction

For a sample of 2361 patients admitted with suspected acute myocardial infarction to a set of 37 hospitals, recorded patient response variables include eligibility for treatment with aspirin, eligibility for treatment with thrombolytics, treatment with aspirin received, treatment with thrombolytics received and short-term patient survival. Each of these five variables has two levels resulting in a 25 contingency table. The covariate information includes age, sex, race and comorbidity status. Because the responses arrive in sequence, we model these data in three stages: eligibility, then treatment received given eligibility and finally short-term survival given eligibility and treatment received, all given the covariates. Issues of interest include the extent to which the treatment received matches eligibility, whether the probability of survival is affected by treatment status and how the chance of mortality is affected by whether or not the treatment received matches eligibility. The influence of covariate information on these quantities is examined. These quantities are studied at the hospital level adjusted for case mix and also in aggregate, marginalizing over hospitals.

Duke Scholars

Author Alan E. Gelfand Statistical Science