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To illustrate the sequence of steps needed to develop and validate a clinical prediction model, when missing predictor values have been multiply imputed.We used data from consecutive primary care patients suspected of deep venous thrombosis (DVT) to develop and validate a diagnostic model for the presence of DVT. Missing values were imputed 10 times with the MICE conditional imputation method. After the selection of predictors and transformations for continuous predictors according to three different methods, we estimated regression coefficients and performance measures.The three methods to select predictors and transformations of continuous predictors showed similar results. Rubin's rules could easily be applied to estimate regression coefficients and performance measures, once predictors and transformations were selected.We provide a practical approach for model development and validation with multiply imputed data.

Type

Journal article

Journal

Journal of clinical epidemiology

Publication Date

02/2010

Volume

63

Pages

205 - 214

Addresses

Julius Center for Health Sciences and Primary Care, University Medical Centre Utrecht, Str 6.131, P.O. Box 85500, 3508 GA, Utrecht, The Netherlands. y.vergouwe@umcutrecht.nl

Keywords

Humans, Venous Thrombosis, Data Interpretation, Statistical, Models, Statistical, Risk Factors, Cross-Sectional Studies, Reproducibility of Results, Adult, Aged, Aged, 80 and over, Middle Aged, Primary Health Care, Female, Male