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U.S. Department of Veterans Affairs Panel on Statistics and Analytics on Healthcare Datasets: Challenges and Recommended Strategies

Challenges and strategies in analysis of missing data

Pages 15-23 | Received 09 Feb 2018, Accepted 17 Apr 2018, Published online: 06 Dec 2019

References

  • Little RJ, D’Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. New England J Med. 2012;367:1355–1360. doi: 10.1056/NEJMsr1203730
  • Molenberghs G., Kenward MG. Missing data in clinical studies. Chichester: John Wiley and Sons; 2007.
  • Zhou XH, Zhou C, Lui D, et al. Applied missing data methods in health sciences. Hoboken: John Wiley and Sons; 2014.
  • Afifi A, Elashoff R. Missing observations in multivariate statistics I: review of the literature. J Am Stat Assoc. 1966;61:595–604.
  • Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J R Stat Soc. Series B (Statistical Methodology). 1977;39(1):1–38.
  • Little RJA, Rubin DB. Statistical analysis with missing data. 2nd ed. Hoboken, NJ: Wiley & Sons; 2002.
  • Heitjan DF, Rubin DB. Ignorability and coarse data. Ann Stat. 1991;19(4):2244–2253. doi: 10.1214/aos/1176348396
  • Robins JM, Rotnitzky AG. Semiparametric efficiency in multivariate regression models with missing data. J Am Stat Assoc. 1995;90(429):122–129. doi: 10.1080/01621459.1995.10476494
  • Tsiatis AA. Semiparametric theory and missing data. New York: Springer; 2006.
  • Schafer JL. Analysis of incomplete multivariate data. London: Chapman and Hall; 1997.
  • Carpenter J, Kenward M. Multiple imputation and its application. Chichester: Wiley & Sons; 2003.
  • Schafer JL, Graham JW. Missing data: our view of the state of the art. Psychol Methods. 2002;7(2):147–177. doi: 10.1037/1082-989X.7.2.147
  • Rubin DB. Multiple imputation for nonresponse in surveys. New York: John Wiley & Sons; 1987.
  • Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw. 2011;45:3. doi: 10.18637/jss.v045.i03
  • McLachlan GJ, Krishnan T. The EM algorithm and extensions. 2nd ed. New York: Wiley; 2008.
  • Wei GCG, Tanner MA. A Monte Carlo implementation of the EM algorithm and the poor man’s data augmentation algorithms. J Am Stat Assoc. 1990;85:699–704. doi: 10.1080/01621459.1990.10474930
  • Gu MG, Zhu HT. Maximum likelihood estimation for spatial models by Markov Chain Monte Carlo stochastic approximation. J R Stat Soc: Series B (Statistical Methodology). 2001;63:339–355. doi: 10.1111/1467-9868.00289
  • Jank WS. Quasi-Monte Carlo sampling to improve the efficiency of Monte Carlo EM. Comput Stat Data Anal. 2005;48:685–701. doi: 10.1016/j.csda.2004.03.019

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