title: Tilting the lasso by knowledge-based post-processing.
abstract: BACKGROUND
  It is useful to incorporate biological knowledge on the role of genetic determinants in predicting an outcome. It is, however, not always feasible to fully elicit this information when the number of determinants is large. We present an approach to overcome this difficulty. First, using half of the available data, a shortlist of potentially interesting determinants are generated. Second, binary indications of biological importance are elicited for this much smaller number of determinants. Third, an analysis is carried out on this shortlist using the second half of the data.
  
  
  RESULTS
  We show through simulations that, compared with adaptive lasso, this approach leads to models containing more biologically relevant variables, while the prediction mean squared error (PMSE) is comparable or even reduced. We also apply our approach to bone mineral density data, and again final models contain more biologically relevant variables and have reduced PMSEs.
  
  
  CONCLUSIONS
  Our method leads to comparable or improved predictive performance, and models with greater face validity and interpretability with feasible incorporation of biological knowledge into predictive models.
authors: Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
authors: Kukatharmini
authors: K
authors: Tharmaratnam
authors: Institute of Population Health, The University of Manchester, Manchester, UK. matthew.sperrin@manchester.ac.uk.
authors: Matthew
authors: http://orcid.org/0000-0002-5351-9960
authors: M
authors: Sperrin
authors: Department of Mathematics and Statistics, Lancaster University, Lancaster, UK.
authors: Thomas
authors: T
authors: Jaki
authors: Department of Medical Biochemistry, Oslo University Hospital, Oslo, Norway.|Lovisenberg Diakonale Hospital, Oslo, Norway.|Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.
authors: Sjur
authors: S
authors: Reppe
authors: Oslo Centre for Biostatistics and Epidemiology, University of Oslo and Oslo University Hospital, Oslo, Norway.
authors: Arnoldo
authors: A
authors: Frigessi
mesh_terms: D000465:Algorithms; D015519:Bone Density / Q000235:genetics; D003198:Computer Simulation; D006801:Humans; D019359:Knowledge; D008962:Models, Theoretical; D015203:Reproducibility of Results
keywords: Bone mineral density; Elicitation; Lasso