Austin, PC;
Lee, DS;
Ko, DT;
White, IR;
(2019)
Effect of Variable Selection Strategy on the Performance of Prognostic Models When Using Multiple Imputation.
Circulation: Cardiovascular Quality and Outcomes
, 12
(11)
, Article e005927. 10.1161/CIRCOUTCOMES.119.005927.
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Abstract
BACKGROUND: Variable selection is an important issue when developing prognostic models. Missing data occur frequently in clinical research. Multiple imputation is increasingly used to address the presence of missing data in clinical research. The effect of different variable selection strategies with multiply imputed data on the external performance of derived prognostic models has not been well examined. METHODS AND RESULTS: We used backward variable selection with 9 different ways to handle multiply imputed data in a derivation sample to develop logistic regression models for predicting death within 1 year of hospitalization with an acute myocardial infarction. We assessed the prognostic accuracy of each derived model in a temporally distinct validation sample. The derivation and validation samples consisted of 11524 patients hospitalized between 1999 and 2001 and 7889 patients hospitalized between 2004 and 2005, respectively. We considered 41 candidate predictor variables. Missing data occurred frequently, with only 13% of patients in the derivation sample and 31% of patients in the validation sample having complete data. Regardless of the significance level for variable selection, the prognostic model developed using only the complete cases in the derivation sample had substantially worse performance in the validation sample than did the models for which variables were selected using the multiply imputed versions of the derivation sample. The other 8 approaches to handling multiply imputed data resulted in prognostic models with performance similar to one another. CONCLUSIONS: Ignoring missing data and using only subjects with complete data can result in the derivation of prognostic models with poor performance. Multiple imputation should be used to account for missing data when developing prognostic models.
| Type: | Article |
|---|---|
| Title: | Effect of Variable Selection Strategy on the Performance of Prognostic Models When Using Multiple Imputation |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1161/CIRCOUTCOMES.119.005927 |
| Publisher version: | https://doi.org/10.1161/CIRCOUTCOMES.119.005927 |
| Language: | English |
| Additional information: | Circulation: Cardiovascular Quality and Outcomes is published on behalf of the American Heart Association, Inc., by Wolters Kluwer Health, Inc. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution, and reproduction in any medium, provided that the original work is properly cited. |
| Keywords: | death, hospitalization, incidence, myocardial infarction, probability |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Inst of Clinical Trials and Methodology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10087282 |
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