Evaluation of an optimal receiver operating characteristic procedure

Abstract

Lu and Elston have recently proposed a procedure for developing optimal receiver operating characteristic curves that maximize the area under a receiver operating characteristic curve in the setting of a predictive genetic test. The method requires only summary data, not individual level genetic data. In an era of increased data sharing, we investigate the performance of this algorithm when individual level genetic data are available and compare this approach to more standard receiver operating characteristic curve-building methods. Conclusion: Though the Lu-Elston method can produce an optimal area under the curve under some assumptions, the method typically has little advantage over standard multivariable logistic methods when data are available. Also, the standard approach easily allows comparison of nested models via likelihood ratio tests and incorporation of covariates - the Lu-Elston approach is shown to have some difficulties with such analyses. These conclusions are based on evaluations using the Genetic Analysis Workshop 16 rheumatoid arthritis data set.