title: Selecting cases for whom additional tests can improve prognostication.
abstract: Prognostic models are increasingly being used in clinical practice. The benefit of adding variables (e.g., gene expression measurements) to an original set of variables (e.g., phenotypes) when building prognostic models is usually measured on a whole set of cases. In practice, however, including additional information only helps build better models for some subsets of cases. It is important to prioritize who should undergo further testing. We present a method that can help identify those patients might benefit from additional testing. Our experiments based on limited breast cancer data indicate that relatively old patients with large tumors and positive lymph nodes constitute a group for whom prognoses can be more accurate with the addition of gene expression measurements. The same is not true for some other groups.
authors.affiliation: Division of Biomedical Informatics, Department of Medicine University of California at San Diego, La Jolla, CA 92093, USA.
authors.forename: Xiaoqian
authors.initials: X
authors.lastname: Jiang
authors.forename: Jihoon
authors.initials: J
authors.lastname: Kim
authors.forename: Yuan
authors.initials: Y
authors.lastname: Wu
authors.forename: Shuang
authors.initials: S
authors.lastname: Wang
authors.forename: Lucila
authors.initials: L
authors.lastname: Ohno-Machado
mesh_terms: D019540:Area Under Curve; D001943:Breast Neoplasms / Q000235:genetics*; D005260:Female; D015870:Gene Expression; D020869:Gene Expression Profiling; D005838:Genotype; D006801:Humans; D016015:Logistic Models; D010641:Phenotype; D011379:Prognosis; D059467:Transcriptome