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 ed. 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 ed. 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). Paper 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.