The search for stable prognostic models in multiple imputed data sets.

Abstract:

BACKGROUND:In prognostic studies model instability and missing data can be troubling factors. Proposed methods for handling these situations are bootstrapping (B) and Multiple imputation (MI). The authors examined the influence of these methods on model composition. METHODS:Models were constructed using a cohort of 587 patients consulting between January 2001 and January 2003 with a shoulder problem in general practice in the Netherlands (the Dutch Shoulder Study). Outcome measures were persistent shoulder disability and persistent shoulder pain. Potential predictors included socio-demographic variables, characteristics of the pain problem, physical activity and psychosocial factors. Model composition and performance (calibration and discrimination) were assessed for models using a complete case analysis, MI, bootstrapping or both MI and bootstrapping. RESULTS:Results showed that model composition varied between models as a result of how missing data was handled and that bootstrapping provided additional information on the stability of the selected prognostic model. CONCLUSION:In prognostic modeling missing data needs to be handled by MI and bootstrap model selection is advised in order to provide information on model stability.

journal_name

BMC Med Res Methodol

authors

Vergouw D,Heymans MW,Peat GM,Kuijpers T,Croft PR,de Vet HC,van der Horst HE,van der Windt DA

doi

10.1186/1471-2288-10-81

subject

Has Abstract

pub_date

2010-09-17 00:00:00

pages

81

issn

1471-2288

pii

1471-2288-10-81

journal_volume

10

pub_type

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