Biomarker studies may involve a multilevel outcome, such as no, mild, or severe disease. There is often interest in predicting one particular level of the outcome due to its clinical significance. The standard approach to constructing biomarker combinations in this context involves dichotomizing the outcome and using a binary logistic regression model. We assessed whether information can be usefully gained from instead using more sophisticated regression methods. Furthermore, it is often necessary to select among several candidate biomarker combinations. One strategy involves selecting a combination on the basis of its ability to predict the outcome level of interest. We propose an algorithm that leverages the multilevel outcome to inform combination selection. We apply this algorithm to data from a study of acute kidney injury after cardiac surgery, where the kidney injury may be absent, mild, or severe. Using more sophisticated modeling approaches to construct combinations provided gains over the binary logistic regression approach in specific settings. In the examples considered, the proposed algorithm for combination selection tended to reduce the impact of bias due to selection and to provide combinations with improved performance. Methods that utilize the multilevel nature of the outcome in the construction and/or selection of biomarker combinations have the potential to yield better combinations.



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