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


CONCLUSIONS
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.
Vergouw
David
D
Institute for Research in Extramural Medicine, VU University Medical Center Amsterdam, The Netherlands. d.vergouw@vumc.nl
Heymans
Martijn W
MW
Peat
George M
GM
Kuijpers
Ton
T
Croft
Peter R
PR
de Vet
Henrica C W
HC
van der Horst
Henriëtte E
HE
van der Windt
Daniëlle A W M
DA
D000328:Adult; D001530:Belgium; D006302:Health Services Research; D006801:Humans; D007407:Interviews as Topic; D008875:Middle Aged; D010821:Physicians, Family / Q000523:psychology*; D019338:Polypharmacy; D012424:Rural Population; D014505:Urban Population