Literature DB >> 19932803

Missing data: what a little can do, and what researchers can do in response.

Thomas R Belin1.   

Abstract

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Year:  2009        PMID： 19932803      PMCID： PMC2900197          DOI： 10.1016/j.ajo.2009.07.027

Source DB:  PubMed          Journal:  Am J Ophthalmol        ISSN： 0002-9394            Impact factor:   5.258


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  1 in total

Review 1.  Multiple imputation: a primer.

Authors:  J L Schafer
Journal:  Stat Methods Med Res       Date:  1999-03       Impact factor: 3.021
  1 in total
  5 in total

1.  Evaluating model-based imputation methods for missing covariates in regression models with interactions.

Authors:  Soeun Kim; Catherine A Sugar; Thomas R Belin
Journal:  Stat Med       Date:  2015-01-29       Impact factor: 2.373

2.  Mi??ing data: should we c?re?

Authors:  Ofer Harel; Jennifer Boyko
Journal:  Am J Public Health       Date:  2012-12-13       Impact factor: 9.308

3.  Multiple Imputation in Three or More Stages.

Authors:  J McGinniss; O Harel
Journal:  J Stat Plan Inference       Date:  2016-04-20       Impact factor: 1.111

Review 4.  Are we missing the importance of missing values in HIV prevention randomized clinical trials? Review and recommendations.

Authors:  Ofer Harel; Jennifer Pellowski; Seth Kalichman
Journal:  AIDS Behav       Date:  2012-08

Review 5.  Big data requirements for artificial intelligence.

Authors:  Sophia Y Wang; Suzann Pershing; Aaron Y Lee
Journal:  Curr Opin Ophthalmol       Date:  2020-09       Impact factor: 3.761
  5 in total

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