There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
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.
Copyright 2010 Elsevier Inc. All rights reserved.