Literature DB >> 22584299

Missing data: a systematic review of how they are reported and handled.

Iris Eekhout1, R Michiel de Boer, Jos W R Twisk, Henrica C W de Vet, Martijn W Heymans.   

Abstract

BACKGROUND: The objectives of this systematic review are to examine how researchers report missing data in questionnaires and to provide an overview of current methods for dealing with missing data.
METHODS: We included 262 studies published in 2010 in 3 leading epidemiologic journals. Information was extracted on how missing data were reported, types of missing, and methods for dealing with missing data.
RESULTS: Seventy-eight percent of the studies lacked clear information about the measurement instruments. Missing data in multi-item instruments were not handled differently from other missing data. Complete-case analysis was most frequently reported (81% of the studies), and the selectivity of missing data was seldom examined.
CONCLUSIONS: Although there are specific methods for handling missing data in item scores and in total scores of multi-item instruments, these are seldom applied. Researchers mainly use complete-case analysis for both types of missing, which may seriously bias the study results.

Mesh:

Year:  2012        PMID: 22584299     DOI: 10.1097/EDE.0b013e3182576cdb

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  41 in total

1.  The role of social media use in improving cancer survivors' emotional well-being: a moderated mediation study.

Authors:  Shaohai Jiang
Journal:  J Cancer Surviv       Date:  2017-01-13       Impact factor: 4.442

2.  Nursing home resident quality of life: testing for measurement equivalence across resident, family, and staff perspectives.

Authors:  Judith Godin; Janice Keefe; E Kevin Kelloway; John P Hirdes
Journal:  Qual Life Res       Date:  2015-04-17       Impact factor: 4.147

Review 3.  An educational review of the statistical issues in analysing utility data for cost-utility analysis.

Authors:  Rachael Maree Hunter; Gianluca Baio; Thomas Butt; Stephen Morris; Jeff Round; Nick Freemantle
Journal:  Pharmacoeconomics       Date:  2015-04       Impact factor: 4.981

Review 4.  Multiple Imputation for Incomplete Data in Environmental Epidemiology Research.

Authors:  Prince Addo Allotey; Ofer Harel
Journal:  Curr Environ Health Rep       Date:  2019-06

5.  Age as a risk factor for early onset of natalizumab-related progressive multifocal leukoencephalopathy.

Authors:  Luca Prosperini; Cristina Scarpazza; Luisa Imberti; Cinzia Cordioli; Nicola De Rossi; Ruggero Capra
Journal:  J Neurovirol       Date:  2017-08-08       Impact factor: 2.643

6.  Missing data in Wave 2 of NSHAP: prevalence, predictors, and recommended treatment.

Authors:  Louise C Hawkley; Masha Kocherginsky; Jaclyn Wong; Juyeon Kim; Kathleen A Cagney
Journal:  J Gerontol B Psychol Sci Soc Sci       Date:  2014-05-08       Impact factor: 4.077

7.  Principled Approaches to Missing Data in Epidemiologic Studies.

Authors:  Neil J Perkins; Stephen R Cole; Ofer Harel; Eric J Tchetgen Tchetgen; BaoLuo Sun; Emily M Mitchell; Enrique F Schisterman
Journal:  Am J Epidemiol       Date:  2018-03-01       Impact factor: 4.897

8.  A methodology for preprocessing structured big data in the behavioral sciences.

Authors:  Paul A Brown; Ricardo A Anderson
Journal:  Behav Res Methods       Date:  2022-06-29

9.  Multiple imputation to deal with missing EQ-5D-3L data: Should we impute individual domains or the actual index?

Authors:  Claire L Simons; Oliver Rivero-Arias; Ly-Mee Yu; Judit Simon
Journal:  Qual Life Res       Date:  2014-12-04       Impact factor: 4.147

10.  Multiple imputation: a mature approach to dealing with missing data.

Authors:  S Chevret; S Seaman; M Resche-Rigon
Journal:  Intensive Care Med       Date:  2015-01-13       Impact factor: 17.440

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.