What impact do assumptions about missing data have on conclusions? A practical sensitivity analysis for a cancer survival registry

Abstract Background Within epidemiological and clinical research, missing data are a common issue and often over looked in publications. When the issue of missing observations is addressed it is usually assumed that the missing data are ‘missing at random’ (MAR). This assumption should be checked fo...

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Autori principali:	M. Smuk, J. R. Carpenter, T. P. Morris
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	BMC 2017-02-01
Serie:	BMC Medical Research Methodology
Soggetti:	Missing data Pattern-mixture model Sensitivity analysis Elicitation Missing at random Missing not at random
Accesso online:	http://link.springer.com/article/10.1186/s12874-017-0301-0
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