Exploring conceptual preprocessing for developing prognostic models: A case study in low back pain patients
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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000400467Publication status
publishedExternal links
Journal / series
Journal of Clinical EpidemiologyVolume
Pages / Article No.
Publisher
ElsevierSubject
Prognostic models; Preprocessing; Subgrouping; Latent class analysis; Low back pain; Lasso; Random forest; Linear modelOrganisational unit
09623 - Feuerriegel, Stefan (ehemalig) / Feuerriegel, Stefan (former)
Funding
186932 - Data-driven health management (SNF)
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