title: A hybrid approach to survival model building using integration of clinical and molecular information in censored data.
abstract: In medical society, the prognostic models, which use clinicopathologic features and predict prognosis after a certain treatment, have been externally validated and used in practice. In recent years, most research has focused on high dimensional genomic data and small sample sizes. Since clinically similar but molecularly heterogeneous tumors may produce different clinical outcomes, the combination of clinical and genomic information, which may be complementary, is crucial to improve the quality of prognostic predictions. However, there is a lack of an integrating scheme for clinic-genomic models due to the P ≥ N problem, in particular, for a parsimonious model. We propose a methodology to build a reduced yet accurate integrative model using a hybrid approach based on the Cox regression model, which uses several dimension reduction techniques, L₂ penalized maximum likelihood estimation (PMLE), and resampling methods to tackle the problem. The predictive accuracy of the modeling approach is assessed by several metrics via an independent and thorough scheme to compare competing methods. In breast cancer data studies on a metastasis and death event, we show that the proposed methodology can improve prediction accuracy and build a final model with a hybrid signature that is parsimonious when integrating both types of variables.
authors.affiliation: Department of Electrical Engineering and Computer Science, Case Western Reserve University, 16000 Terrace Rd #503, Cleveland, OH 44112, USA. ixc27@case.edu
authors.forename: Ickwon
authors.initials: I
authors.lastname: Choi
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
authors.forename: Brian J
authors.initials: BJ
authors.lastname: Wells
authors.forename: Changhong
authors.initials: C
authors.lastname: Yu
mesh_terms: D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D019295:Computational Biology / Q000379:methods*; D016208:Databases, Factual; D005260:Female; D020869:Gene Expression Profiling; D006801:Humans; D008954:Models, Biological; D009362:Neoplasm Metastasis; D020411:Oligonucleotide Array Sequence Analysis; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D015203:Reproducibility of Results