Regression analysis approach for infarctions

To assess the relation between eGFR and infarctions, we used logistic regression models, as few participants had multiple infarctions and the spread of the count of infarctions was small. We report the relative increase in odds of an infarction for a 10% decrease in eGFR or 10% increase in UACR. We fit models with eGFR alone and UACR alone, and finally both eGFR and UACR in the same model. As with the continuous outcome models, we used PML estimation to adjust for age, sex, education, race, diabetes, hypertension, cholesterol, systolic blood pressure, diastolic blood pressure, stroke/TIA, AFIB, CVD, smoking, and alcohol use. We used a penalty that assumed a priori the odds ratio (OR) for scaled covariates was between 0.10 and 10 [20].