title: Further insight into the incremental value of new markers: the interpretation of performance measures and the importance of clinical context.
abstract: In this issue of the Journal, Pencina and et al. (Am J Epidemiol. 2012;176(6):492-494) examine the operating characteristics of measures of incremental value. Their goal is to provide benchmarks for the measures that can help identify the most promising markers among multiple candidates. They consider a setting in which new predictors are conditionally independent of established predictors. In the present article, the authors consider more general settings. Their results indicate that some of the conclusions made by Pencina et al. are limited to the specific scenarios the authors considered. For example, Pencina et al. observed that continuous net reclassification improvement was invariant to the strength of the baseline model, but the authors of the present study show this invariance does not hold generally. Further, they disagree with the suggestion that such invariance would be desirable for a measure of incremental value. They also do not see evidence to support the claim that the measures provide complementary information. In addition, they show that correlation with baseline predictors can lead to much bigger gains in performance than the conditional independence scenario studied by Pencina et al. Finally, the authors note that the motivation of providing benchmarks actually reinforces previous observations that the problem with these measures is they do not have useful clinical interpretations. If they did, researchers could use the measures directly and benchmarks would not be needed.
authors.affiliation: Department of Biostatistics, University of Washington, Seattle, USA. katiek@u.washington.edu
authors.forename: Kathleen F
authors.initials: KF
authors.lastname: Kerr
authors.forename: Aasthaa
authors.initials: A
authors.lastname: Bansal
authors.forename: Margaret S
authors.initials: MS
authors.lastname: Pepe
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D003627:Data Interpretation, Statistical; D006801:Humans; D016015:Logistic Models; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*
chemical_list: D015415:Biomarkers