Exploring conceptual preprocessing for developing prognostic models: a case study in low back pain patients

Anne Molgaard Nielsen, Adrian Binding, Casey Ahlbrandt-Rains, Martin Boeker, Stefan Feuerriegel, Werner Vach

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Objectives: A conceptually oriented preprocessing of a large number of potential prognostic factors may improve the development of a prognostic model. This study investigated whether various forms of conceptually oriented preprocessing or the preselection of established factors was superior to using all factors as input. Study Design and Setting: We made use of an existing project that developed two conceptually oriented subgroupings of low back pain patients. Based on the prediction of six outcome variables by seven statistical methods, this type of preprocessing was compared with medical experts’ preselection of established factors, as well as using all 112 available baseline factors. Results: Subgrouping of patients was associated with low prognostic capacity. Applying a Lasso-based variable selection to all factors or to domain-specific principal component scores performed best. The preselection of established factors showed a good compromise between model complexity and prognostic capacity. Conclusion: The prognostic capacity is hard to improve by means of a conceptually oriented preprocessing when compared to purely statistical approaches. However, a careful selection of already established factors combined in a simple linear model should be considered as an option when constructing a new prognostic rule based on a large number of potential prognostic factors.

Original languageEnglish
Pages (from-to)27-34
Number of pages8
JournalJournal of Clinical Epidemiology
Volume122
DOIs
StatePublished - Jun 2020
Externally publishedYes

Keywords

  • Lasso
  • Latent class analysis
  • Linear model
  • Low back pain
  • Preprocessing
  • Prognostic models
  • Random forest
  • Subgrouping

Fingerprint

Dive into the research topics of 'Exploring conceptual preprocessing for developing prognostic models: a case study in low back pain patients'. Together they form a unique fingerprint.

Cite this