@article{ref1,
title="Missing data in prediction research: a five step approach for multiple imputation, illustrated in the CENTER-TBI study",
journal="Journal of neurotrauma",
year="2021",
author="Gravesteijn, Benjamin and Sewalt, Charlie and Venema, Esmee and Nieboer, Daan and Steyerberg, Ewout W.",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="In medical research, missing data is common. In acute diseases such as traumatic brain injury (TBI), even well conducted prospective studies may suffer from missing data in baseline characteristics and outcomes. Statistical models may simply drop patients with any missing values, potentially leaving a selected subset of the original cohort. Imputation is widely accepted by methodologists as an appropriate way to deal with missing data. We aim to provide practical guidance on handling missing data for prediction modelling. We hereto propose a five-step approach, centred around single and multiple imputation: 1) explore the missing data patterns; 2) choose a method of imputation; 3) perform imputation; 4) assess diagnostics of the imputation; and 5) analyse the imputed datasets. We illustrate these 5 steps with the estimation and validation of the IMPACT prognostic model in 1375 patients from the CENTER-TBI database, included in 53 centers across 17 countries, with moderate or severe TBI in the prospective European CENTER-TBI study. Future prediction modelling studies in acute diseases may benefit from following the suggested 5 steps for optimal statistical analysis and interpretation, after maximal effort have been made to minimize missing data.<p /> <p>Language: en</p>",
language="en",
issn="0897-7151",
doi="10.1089/neu.2020.7218",
url="http://dx.doi.org/10.1089/neu.2020.7218"
}