Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, and Winston Chang. 2018. Rmarkdown: Dynamic Documents for R. https://CRAN.R-project.org/package=rmarkdown.
Allison, P. D. 2002. Missing Data. Thousand Oaks, CA: Sage.
Baraldi, A. N., and C. K. Enders. 2010. “An introduction to modern missing data analyses.” J Sch Psychol 48 (1): 5–37.
Barnard, J., and D. B. Rubin. 1999. “Small-Sample Degrees of Freedom with Multiple Imputation.” Biometrika 86 (4): 948–55.
Bartlett, J. W., S. R. Seaman, I. R. White, and J. R. Carpenter. 2015. “Multiple imputation of covariates by fully conditional specification: Accommodating the substantive model.” Stat Methods Med Res 24 (4): 462–87.
Bodner, T. E. 2008. “What Improves with Increased Missing Data Imputations?” Structural Equation Modeling 15 (4): 651–75.
Box, G. E. P., and G. C. Tiao. 2007. Bayesian Inference in Statistical Analysis. Addison-Wesley Publishing Company.
Brand, J. P. L. 1999. “Development, Implementation and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets.” PhD thesis, Rotterdam: Erasmus University.
Collins, L. M., J. L. Schafer, and C. M. Kam. 2001. “A Comparison of Inclusive and Restrictive Strategies in Modern Missing Data Procedures.” Psychological Methods 6 (3): 330–51.
Eekhout, I., R. M. de Boer, J. W. Twisk, H. C. de Vet, and M. W. Heymans. 2012. “Missing data: a systematic review of how they are reported and handled.” Epidemiology 23 (5): 729–32.
Eekhout, I., H. C. W. De Vet, M. R. De Boer, J. W. R. Twisk, and M. W. Heymans. 2018. “Passive Imputation and Parcel Summaries Are Both Valid to Handle Missing Items in Studies with Many Multi-Item Scales.” Statistical Methods in Medical Research 27 (4): 1128–40.
Eekhout, I., H. C. de Vet, J. W. Twisk, J. P. Brand, M. R. de Boer, and M. W. Heymans. 2014. “Missing data in a multi-item instrument were best handled by multiple imputation at the item score level.” J Clin Epidemiol 67 (3): 335–42.
Eekhout, I., M.A. van de Wiel, and M. W. Heymans. 2017. “Methods for significance testing of categorical covariates in logistic regression models after multiple imputation: power and applicability analysis.” BMC Med Res Methodol 17 (1): 129.
Enders, Craig K. 2010. Applied Missing Data Analysis. Guilford Press.
Gelman, Andrew, John B Carlin, Hal S Stern, and Donald B Rubin. 2014. Bayesian Data Analysis. Vol. 2. Taylor; Francis.
Graham, J. W., A. E. Olchowski, and T. D. Gilreath. 2007. “How Many Imputations Are Really Needed? Some Practical Clarifications of Multiple Imputation Theory.” Preventive Science 8 (3): 206–13.
Hill, M. n.d. SPSS Missing Value Analysis. SPSS Inc.
Hippel, P.T. von. 2004. “Biases in SPSS 12.0 Missing Values Analysis.” The American Statistician 58 (2): 160–64.
Li P, Allison DB, Stuart EA. 2015. “Multiple Imputation: A Flexible Tool for Handling Missing Data.” JAMA 314 (18): 1966–7. doi:10.1001/jama.2015.15281.
Little, R. J. A. 1988. “A Test of Missing Completely at Random for Multivariate Data with Missing Values.” Journal of the American Statistical Association 83 (404): 1198–1202.
Little, R.J., R. D’Agostino, M.L. Cohen, K. Dickersin, S.S. Emerson, J.T. Farrar, C. Frangakis, et al. 2012. “The Prevention and Treatment of Missing Data in Clinical Trials.” New England Journal of Medicine 367 (14): 1355–60.
Little, R.J.A., and D.B. Rubin. 2007. Statistical Analysis with Missing Data. Vol. 2. Wiley New York:
Marshall, Andrea, Douglas G Altman, Roger L Holder, and Patrick Royston. 2009. “Combining Estimates of Interest in Prognostic Modelling Studies After Multiple Imputation: Current Practice and Guidelines.” BMC Medical Research Methodology 9: 57.
Meng, X. L., and D. B. Rubin. 1992. “Performing Likelihood Ratio Tests with Multiply-Imputed Data Sets.” Biometrika 79 (1): 103–11.
Mistler, S.A. 2013. “A Sas Macro for Computing Pooled Likelihood Ratio Tests with Multiply Imputed Data.” Proceedings of the SAS Global Forum 2013, San Francisco, California: Contributed Paper (Statistics and Data Analysis), no. 438.
Moons, K. G., D. G. Altman, J. B. Reitsma, J. P. Ioannidis, P. Macaskill, E. W. Steyerberg, A. J. Vickers, D. F. Ransohoff, and G. S. Collins. 2015. “Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration.” Ann. Intern. Med. 162 (1): 1–73.
Raghunathan, T. 2016. Missing Data Analysis in Practice. Boca Raton, FL: Boca Raton: CRC Press.
Ridout, M S. 1991. “Testing for Random Dropouts in Repeated Measurement Data.” Biometrics 47 (4): 1617–9; discussion 1619–21.
Rubin, D. B. 1976. “Inference and Missing Data.” Biometrika 63 (3): 581–90.
———. 1987. Multiple Imputation for Nonresponse in Surveys. Wiley.
Sterne, Jonathan AC, Ian R White, John B Carlin, Michael Spratt, Patrick Royston, Michael G Kenward, Angela M Wood, and James R Carpenter. 2009. “Multiple Imputation for Missing Data in Epidemiological and Clinical Research: Potential and Pitfalls.” BMJ: British Medical Journal 338.
Twisk, J. W. R. 2006. Applied Multilevel Analysis. Cambridge: CUP.
Van Buuren, S. 2018. Flexible Imputation of Missing Data. Second Edition. Boca Raton, FL: Chapman & Hall/CRC.
White, I. R., P. Royston, and A. M. Wood. 2011. “Multiple imputation using chained equations: Issues and guidance for practice.” Stat Med 30 (4): 377–99.
Wood, A. M., I. R. White, and P. Royston. 2008. “How Should Variable Selection Be Performed with Multiply Imputed Data?” Statistics in Medicine 27 (17): 3227–46.
Xie, Yihui. 2018. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.