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

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• 作者：M. Smuk ; J. R. Carpenter ; T. P. Morris
• 关键词：Missing data ; Pattern ; mixture model ; Sensitivity analysis ; Elicitation ; Missing at random ; Missing not at random
• 刊名：BMC Medical Research Methodology
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：17
• 期：1
• 全文大小：1352KB
• 刊物主题：Theory of Medicine/Bioethics; Statistical Theory and Methods; Statistics for Life Sciences, Medicine, Health Sciences;
• 出版者：BioMed Central
• ISSN：1471-2288
• 卷排序：17

文摘

BackgroundWithin 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 for plausibility, however it is untestable, thus inferences should be assessed for robustness to departures from missing at random.