(Q39865619)

15 September 2017

title

Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach (English)

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15 September 2017

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Nicole S Erler

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Oscar H Franco

object named as

Oscar H Franco

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Vincent W Jaddoe

4

object named as

Vincent W V Jaddoe

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15 September 2017

Dimitris Rizopoulos

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15 September 2017

Joost van Rosmalen

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15 September 2017

Emmanuel M E H Lesaffre

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15 September 2017

language of work or name

English

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publication date

4 April 2016

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15 September 2017

published in

Statistics in Medicine

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Review: a gentle introduction to imputation of missing values

15 September 2017

cites work

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Missing covariate data in medical research: to impute is better than to ignore

21 January 2018

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Flexible Imputation of Missing Data

21 January 2018

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Using the outcome for imputation of missing predictor values was preferred.

21 January 2018

1 reference

Maximum likelihood methods for nonignorable missing responses and covariates in random effects models

21 January 2018

1 reference

Doubly robust estimates for binary longitudinal data analysis with missing response and missing covariates

21 January 2018

1 reference

The Generation R Study: design and cohort update 2012.

21 January 2018

1 reference

Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model

21 January 2018

1 reference

Maximum likelihood methods for cure rate models with missing covariates.

21 January 2018

1 reference

Fitting Linear Mixed-Effects Models Using lme4

21 January 2018

1 reference

Latent class model diagnosis.

21 January 2018

1 reference

Multiple imputation using chained equations: Issues and guidance for practice

21 January 2018

1 reference

Multiple Imputation and its Application

21 January 2018

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Multilevel models with multivariate mixed response types

21 January 2018

1 reference

21 January 2018

Identifiers

DOI

10.1002/SIM.6944

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15 September 2017

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