Literature DB >> 31346889

The validity of propensity score analysis using complete cases with partially observed covariates.

Byeong Yeob Choi1, Jonathan Gelfond2.   

Abstract

Entities:  

Keywords:  Complete case analysis; Homogeneous treatment effect; Missing data; Multiple imputation; Propensity score

Mesh：

Year:  2019        PMID： 31346889      PMCID： PMC8098813          DOI： 10.1007/s10654-019-00538-x

Source DB:  PubMed          Journal:  Eur J Epidemiol        ISSN： 0393-2990            Impact factor:   8.082


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  4 in total

1.  Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values.

Authors:  Ian R White; John B Carlin
Journal:  Stat Med       Date:  2010-12-10       Impact factor: 2.373

2.  The performance of different propensity score methods for estimating marginal odds ratios.

Authors:  Peter C Austin
Journal:  Stat Med       Date:  2007-07-20       Impact factor: 2.373

3.  Propensity score analysis with partially observed covariates: How should multiple imputation be used?

Authors:  Clémence Leyrat; Shaun R Seaman; Ian R White; Ian Douglas; Liam Smeeth; Joseph Kim; Matthieu Resche-Rigon; James R Carpenter; Elizabeth J Williamson
Journal:  Stat Methods Med Res       Date:  2017-06-02       Impact factor: 3.021

4.  A comparison of different methods to handle missing data in the context of propensity score analysis.

Authors:  Jungyeon Choi; Olaf M Dekkers; Saskia le Cessie
Journal:  Eur J Epidemiol       Date:  2018-10-19       Impact factor: 8.082
  4 in total
  1 in total

1.  Propensity Score Analysis with Partially Observed Baseline Covariates: A Practical Comparison of Methods for Handling Missing Data.

Authors:  Daniele Bottigliengo; Giulia Lorenzoni; Honoria Ocagli; Matteo Martinato; Paola Berchialla; Dario Gregori
Journal:  Int J Environ Res Public Health       Date:  2021-06-22       Impact factor: 3.390
  1 in total

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