The MI Procedure

Allison, P. D. (2000), “Multiple Imputation for Missing Data: A Cautionary Tale,” Sociological Methods and Research, 28, 301–309.

Allison, P. D. (2001), Missing Data, Thousand Oaks, CA: Sage Publications.

Anderson, T. W. (1984), An Introduction to Multivariate Statistical Analysis, 2nd Edition, New York: John Wiley & Sons.

Barnard, J. and Meng, X. L. (1999), “Applications of Multiple Imputation in Medical Studies: From AIDS to NHANES,” Statistical Methods in Medical Research, 8, 17–36.

Barnard, J. and Rubin, D. B. (1999), “Small-Sample Degrees of Freedom with Multiple Imputation,” Biometrika, 86, 948–955.

Brand, J. P. L. (1999), Development, Implementation, and Evaluation of Multiple Imputation Strategies for the Statistical Analysis of Incomplete Data Sets, Ph.D. thesis, Erasmus University.

Carpenter, J. R. and Kenward, M. G. (2013), Multiple Imputation and Its Application, New York: John Wiley & Sons.

Dempster, A. P., Laird, N. M., and Rubin, D. B. (1977), “Maximum Likelihood from Incomplete Data via the EM Algorithm,” Journal of the Royal Statistical Society, Series B, 39, 1–38.

Gadbury, G. L., Coffey, C. S., and Allison, D. B. (2003), “Modern Statistical Methods for Handling Missing Repeated Measurements in Obesity Trial Data: Beyond LOCF,” Obesity Reviews, 4, 175–184.

Gelman, A. and Rubin, D. B. (1992), “Inference from Iterative Simulation Using Multiple Sequences,” Statistical Science, 7, 457–472.

Goodnight, J. H. (1979), “A Tutorial on the Sweep Operator,” American Statistician, 33, 149–158.

Heitjan, F. and Little, R. J. A. (1991), “Multiple Imputation for the Fatal Accident Reporting System,” Applied Statistics, 40, 13–29.

Horton, N. J. and Lipsitz, S. R. (2001), “Multiple Imputation in Practice: Comparison of Software Packages for Regression Models with Missing Variables,” American Statistician, 55, 244–254.

Lavori, P. W., Dawson, R., and Shera, D. (1995), “A Multiple Imputation Strategy for Clinical Trials with Truncation of Patient Data,” Statistics in Medicine, 14, 1913–1925.

Li, K. H. (1988), “Imputation Using Markov Chains,” Journal of Statistical Computation and Simulation, 30, 57–79.

Li, K. H., Raghunathan, T. E., and Rubin, D. B. (1991), “Large-Sample Significance Levels from Multiply Imputed Data Using Moment-Based Statistics and an F Reference Distribution,” Journal of the American Statistical Association, 86, 1065–1073.

Little, R. J. A. (1993), “Pattern-Mixture Models for Multivariate Incomplete Data,” Journal of the American Statistical Association, 88, 125–134.

Little, R. J. A. (1995), “Modeling the Drop-Out Mechanism in Repeated-Measures Studies,” Journal of the American Statistical Association, 90, 1112–1121.

Little, R. J. A. and Rubin, D. B. (2002), Statistical Analysis with Missing Data, 2nd Edition, Hoboken, NJ: John Wiley & Sons.

Liu, C. (1993), “Bartlett’s Decomposition of the Posterior Distribution of the Covariance for Normal Monotone Ignorable Missing Data,” Journal of Multivariate Analysis, 46, 198–206.

McLachlan, G. J. and Krishnan, T. (1997), The EM Algorithm and Extensions, New York: John Wiley & Sons.

Molenberghs, G. and Kenward, M. G. (2007), Missing Data in Clinical Studies, New York: John Wiley & Sons.

National Research Council (2010), The Prevention and Treatment of Missing Data in Clinical Trials, Panel on Handling Missing Data in Clinical Trials, Committee on National Statistics, Division of Behavioral and Social Sciences and Education, Washington, DC: National Academies Press.

O’Neill, R. T. and Temple, R. (2012), “The Prevention and Treatment of Missing Data in Clinical Trials: An FDA Perspective on the Importance of Dealing with It,” Clinical Pharmacology and Therapeutics, 91, 550–554.

Ratitch, B. and O’Kelly, M. (2011), “Implementation of Pattern-Mixture Models Using Standard SAS/STAT Procedures,” in Proceedings of PharmaSUG 2011 (Pharmaceutical Industry SAS Users Group), SP04, Nashville.

Rosenbaum, P. R. and Rubin, D. B. (1983), “The Central Role of the Propensity Score in Observational Studies for Causal Effects,” Biometrika, 70, 41–55.

Rubin, D. B. (1976), “Inference and Missing Data,” Biometrika, 63, 581–592.

Rubin, D. B. (1987), Multiple Imputation for Nonresponse in Surveys, New York: John Wiley & Sons.

Rubin, D. B. (1996), “Multiple Imputation after 18+ Years,” Journal of the American Statistical Association, 91, 473–489.

Schafer, J. L. (1997), Analysis of Incomplete Multivariate Data, New York: Chapman & Hall.

Schafer, J. L. (1999), “Multiple Imputation: A Primer,” Statistical Methods in Medical Research, 8, 3–15.

Schenker, N. and Taylor, J. M. G. (1996), “Partially Parametric Techniques for Multiple Imputation,” Computational Statistics and Data Analysis, 22, 425–446.

Tanner, M. A. and Wong, W. H. (1987), “The Calculation of Posterior Distributions by Data Augmentation,” Journal of the American Statistical Association, 82, 528–540.

Van Buuren, S. (2007), “Multiple Imputation of Discrete and Continuous Data by Fully Conditional Specification,” Statistical Methods in Medical Research, 16, 219–242.

Van Buuren, S. (2012), Flexible Imputation of Missing Data, Boca Raton, FL: Chapman & Hall/CRC.

Van Buuren, S., Boshuizen, H. C., and Knook, D. L. (1999), “Multiple Imputation of Missing Blood Pressure Covariates in Survival Analysis,” Statistics in Medicine, 18, 681–694.