TY  - JOUR
PY  - 2021//
TI  - Missing data in prediction research: a five step approach for multiple imputation, illustrated in the CENTER-TBI study
JO  - Journal of neurotrauma
A1  - Gravesteijn, Benjamin
A1  - Sewalt, Charlie
A1  - Venema, Esmee
A1  - Nieboer, Daan
A1  - Steyerberg, Ewout W.
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - 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>
LA  - en
SN  - 0897-7151
UR  - http://dx.doi.org/10.1089/neu.2020.7218
ID  - ref1
ER  -