The MIANALYZE 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.

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

  • Cochran, W. G. (1977), Sampling Techniques, 3rd Edition, New York: John Wiley & Sons.

  • 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.

  • 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.

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

  • 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.

  • 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.