What do we do with missing data? Some options for analysis of incomplete data.

By Trivellore E. Raghunathan. Annual Review of Public Health, 2004

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An introduction to modern missing data analyses. (2010, background): “Because MCAR requires missingness to be unrelated to study variables, methodologists often argue that it is a very strict assumption that is unlikely to be satisfied in practice (Raghunathan, 2004; Muthen, Kaplan, & Hollis, 1987).”
A life course approach to chronic disease epidemiology. (2005, background): “Finally, long-term life course studies of chronic disease will make the problems of missing data even more acute than they are currently, and so advances in multiple imputation techniques are likely to be important (157).”
Performance of Estimators for Confirmatory Factor Analysis of Ordinal Variables with Missing Data (2020, background): “However, many methodologists argue that the MCAR assumption is rarely tenable (e.g., Little, Jorgensen, Lang, & Moore, 2013; Muthén, Kaplan, & Hollis, 1987; Raghunathan, 2004).”
Much Ado About Nothing (2007, background): “Publications by Meng (2000) and Raghunathan (2004) provide a general introduction, and the paper by Ibrahim et al. (2005) reviews recent developments in a comprehensive fashion, though their application (cancer dataset) features incompleteness on only one variable.”
Working With Missing Values (2005, background): “A variable is a mechanism if it helps to explain whether or not a respondent answers a question ( Raghunathan, 2004; Schafer, 1997).”