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Missing Data in Pre...
Missing Data in Prediction Research : A Five-Step Approach for Multiple Imputation, Illustrated in the CENTER-TBI Study
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- Gravesteijn, Benjamin Yaël (författare)
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
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- Sewalt, Charlie Aletta (författare)
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
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- Venema, Esmee (författare)
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
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- Nieboer, Daan (författare)
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands
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- Steyerberg, Ewout W. (författare)
- Department of Public Health, Erasmus Medical Center, Rotterdam, The Netherlands; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands
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- Brorsson, Camilla (författare)
- Umeå universitet,Anestesiologi och intensivvård
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- Brorsson, Camilla (bidragsgivare)
- Umeå universitet,Anestesiologi och intensivvård
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- Koskinen, Lars-Owe D., Professor, 1955- (bidragsgivare)
- Umeå universitet,Institutionen för klinisk vetenskap
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- Sundström, Nina (bidragsgivare)
- Umeå universitet,Institutionen för strålningsvetenskaper
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- Oresic, Matej, 1967- (bidragsgivare)
- Örebro universitet,Institutionen för medicinska vetenskaper,CENTER-TBI Collaborators
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(creator_code:org_t)
- Mary Ann Liebert, 2021
- 2021
- Engelska.
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Ingår i: Journal of Neurotrauma. - : Mary Ann Liebert. - 0897-7151 .- 1557-9042. ; 38:13, s. 1842-1857
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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https://urn.kb.se/re...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- 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.
Ämnesord
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Anestesi och intensivvård (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Anesthesiology and Intensive Care (hsv//eng)
- MEDICIN OCH HÄLSOVETENSKAP -- Klinisk medicin -- Neurologi (hsv//swe)
- MEDICAL AND HEALTH SCIENCES -- Clinical Medicine -- Neurology (hsv//eng)
Nyckelord
- imputation
- missing data
- prediction
- traumatic brain injury
- tutorial
Publikations- och innehållstyp
- ref (ämneskategori)
- art (ämneskategori)
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Till lärosätets databas
- Av författaren/redakt...
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Gravesteijn, Ben ...
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Sewalt, Charlie ...
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Venema, Esmee
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Nieboer, Daan
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Steyerberg, Ewou ...
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Brorsson, Camill ...
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Koskinen, Lars-O ...
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Sundström, Nina
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Oresic, Matej, 1 ...
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- Om ämnet
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Anestesi och int ...
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- MEDICIN OCH HÄLSOVETENSKAP
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MEDICIN OCH HÄLS ...
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och Klinisk medicin
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och Neurologi
- Artiklar i publikationen
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Journal of Neuro ...
- Av lärosätet
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Umeå universitet
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Örebro universitet
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Karolinska Institutet