Missing data: A statistical framework for practice

Missing data are ubiquitous in medical research, yet there is still uncertainty over when restricting to the complete records is likely to be acceptable, when more complex methods (e.g. maximum likelihood, multiple imputation and Bayesian methods) should be used, how they relate to each other and th...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Biometrical journal Ročník 63; číslo 5; s. 915 - 947
Hlavní autoři: Carpenter, James R., Smuk, Melanie
Médium: Journal Article
Jazyk:angličtina
Vydáno: Germany Wiley - VCH Verlag GmbH & Co. KGaA 01.06.2021
Témata:
Bayes Theorem
Bayesian analysis
Case-Control Studies
Cohort Studies
complete records
Data Interpretation, Statistical
Humans
Medical research
Missing data
multiple imputation
Sensitivity analysis
Uncertainty
multiple imputation
sensitivity analysis
complete records
missing data
ISSN:0323-3847, 1521-4036, 1521-4036
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Missing data are ubiquitous in medical research, yet there is still uncertainty over when restricting to the complete records is likely to be acceptable, when more complex methods (e.g. maximum likelihood, multiple imputation and Bayesian methods) should be used, how they relate to each other and the role of sensitivity analysis. This article seeks to address both applied practitioners and researchers interested in a more formal explanation of some of the results. For practitioners, the framework, illustrative examples and code should equip them with a practical approach to address the issues raised by missing data (particularly using multiple imputation), alongside an overview of how the various approaches in the literature relate. In particular, we describe how multiple imputation can be readily used for sensitivity analyses, which are still infrequently performed. For those interested in more formal derivations, we give outline arguments for key results, use simple examples to show how methods relate, and references for full details. The ideas are illustrated with a cohort study, a multi‐centre case control study and a randomised clinical trial.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ObjectType-Review-3
content type line 23
ISSN:0323-3847
1521-4036
1521-4036
DOI:10.1002/bimj.202000196