Missing Data in Prediction Research: A Five-Step Approach for Multiple Imputation,  Illustrated in the CENTER-TBI Study.

Biomedical Research/methods/statistics & numerical data; Brain Injuries, Traumatic/diagnosis/epidemiology; Cohort Studies; Data Interpretation, Statistical; Databases, Factual/statistics & numerical data; Europe/epidemiology; Humans; Prognosis; Prospective Studies; imputation; missing data; prediction; traumatic brain injury; tutorial

Résumé :

[en] 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 modeling. We hereto propose a five-step approach, centered 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) analyze the imputed data sets. We illustrate these five steps with the estimation and validation of the IMPACT (International Mission on Prognosis and Analysis of Clinical Trials in Traumatic Brain Injury) 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 modeling studies in acute diseases may benefit from following the suggested five steps for optimal statistical analysis and interpretation, after maximal effort has been made to minimize missing data.

Disciplines :

Sciences de la santé humaine: Multidisciplinaire, généralités & autres

Auteur, co-auteur :

Gravesteijn, Benjamin Yaël; Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.

Sewalt, Charlie Aletta; Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.

Venema, Esmee; Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.

Nieboer, Daan; Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands.

Steyerberg, Ewout W; Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands. ; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden,

CENTER-TBI, Collaborators

Autre collaborateur :

Åkerlund, Cecilia

Amrein, Krisztina

Andelic, Nada

Andreassen, Lasse

Anke, Audny

Antoni, Anna

Audibert, Gérard

Azouvi, Philippe

Azzolini, Maria Luisa

Bartels, Ronald

Plus d'auteurs (230 en +)

Langue du document :

Anglais

Titre :

Missing Data in Prediction Research: A Five-Step Approach for Multiple Imputation, Illustrated in the CENTER-TBI Study.

Date de publication/diffusion :

01 juin 2021

Titre du périodique :

Journal of Neurotrauma

ISSN :

0897-7151

eISSN :

1557-9042

Volume/Tome :

Fascicule/Saison :

Pagination :

1842-1857

Peer reviewed :

Peer reviewed vérifié par ORBi

Disponible sur ORBi :

depuis le 27 mars 2022

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1674 (dont 4 ULiège)

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