Journal of Data Science

Abstract: Multiple binary outcomes that measure the presence or absence of medical conditions occur frequently in public health survey research. The multiple possibly correlated binary outcomes may compose of a syndrome or a group of related diseases. It is often of scientific interest to model the interrelationships not only between outcome and risk factors, but also between different outcomes. Applied and practical methods dealing with multiple outcomes from complex designed surveys are lacking. We propose a multivariate approach based on the generalized estimating equation (GEE) methodology to simultaneously conduct survey logistic regressions for each binary outcome in a single analysis. The approach has the following attrac tive features: 1) It enables modeling the complete information from multiple outcomes in a single analysis; 2) it permits to test the correlations between multiple binary outcomes; 3) it allows of discerning the outcome-specific ef fect and the overall risk factor effect; and 4) it provides the measurement of difference of the association between risk factors and multiple outcomes. The proposed method is applied to a study on risk factors for heart attack and stroke in 2009 U.S. nationwide Behavioral Risk Factor Surveillance System (BRFSS) data.