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Nickolaj Risbo

Missing data and multiple imputation in clinical epidemiological research

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Missing data and multiple imputation in clinical epidemiological research. / Pedersen, Alma Becic; Mikkelsen, Ellen Margrethe; Cronin Fenton, Deirdre; Kristensen, Nickolaj Risbo; Pham, Tra My; Pedersen, Lars; Petersen, Irene.

In: Clinical epidemiology, Vol. 9, 2017, p. 157-166.

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@article{4f526d3ca3824f828172f502691c5583,
title = "Missing data and multiple imputation in clinical epidemiological research",
abstract = "Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological research, missing data are seldom MCAR. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. A number of methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the MCAR assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data. Multiple imputation is implemented in most statistical software under the MAR assumption and provides unbiased and valid estimates of associations based on information from the available data. The method affects not only the coefficient estimates for variables with missing data but also the estimates for other variables with no missing data.",
keywords = "Journal Article",
author = "Pedersen, {Alma Becic} and Mikkelsen, {Ellen Margrethe} and {Cronin Fenton}, Deirdre and Kristensen, {Nickolaj Risbo} and Pham, {Tra My} and Lars Pedersen and Irene Petersen",
year = "2017",
doi = "10.2147/CLEP.S129785",
language = "English",
volume = "9",
pages = "157--166",
journal = "Clinical Epidemiology",
issn = "1179-1349",
publisher = "Dove Medical Press Ltd.(Dovepress)",

}

RIS

TY - JOUR

T1 - Missing data and multiple imputation in clinical epidemiological research

AU - Pedersen, Alma Becic

AU - Mikkelsen, Ellen Margrethe

AU - Cronin Fenton, Deirdre

AU - Kristensen, Nickolaj Risbo

AU - Pham, Tra My

AU - Pedersen, Lars

AU - Petersen, Irene

PY - 2017

Y1 - 2017

N2 - Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological research, missing data are seldom MCAR. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. A number of methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the MCAR assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data. Multiple imputation is implemented in most statistical software under the MAR assumption and provides unbiased and valid estimates of associations based on information from the available data. The method affects not only the coefficient estimates for variables with missing data but also the estimates for other variables with no missing data.

AB - Missing data are ubiquitous in clinical epidemiological research. Individuals with missing data may differ from those with no missing data in terms of the outcome of interest and prognosis in general. Missing data are often categorized into the following three types: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). In clinical epidemiological research, missing data are seldom MCAR. Missing data can constitute considerable challenges in the analyses and interpretation of results and can potentially weaken the validity of results and conclusions. A number of methods have been developed for dealing with missing data. These include complete-case analyses, missing indicator method, single value imputation, and sensitivity analyses incorporating worst-case and best-case scenarios. If applied under the MCAR assumption, some of these methods can provide unbiased but often less precise estimates. Multiple imputation is an alternative method to deal with missing data, which accounts for the uncertainty associated with missing data. Multiple imputation is implemented in most statistical software under the MAR assumption and provides unbiased and valid estimates of associations based on information from the available data. The method affects not only the coefficient estimates for variables with missing data but also the estimates for other variables with no missing data.

KW - Journal Article

U2 - 10.2147/CLEP.S129785

DO - 10.2147/CLEP.S129785

M3 - Journal article

VL - 9

SP - 157

EP - 166

JO - Clinical Epidemiology

JF - Clinical Epidemiology

SN - 1179-1349

ER -