References
- R. J.A., Little, D. B., Rubin, Statistical Analysis with Missing Data, 2nd ed., Hoboken, NJ: Wiley 2002.
- A., MacKinnon, The use and reporting of multiple imputation in medical research: A review. J. Intern. Med., 268(6) (2010), 586–593. doi: 10.1111/j.1365-2796.2010.02274.x
- J. A.C., Sterne, I. R., White, J. B., Carlin, M., Spratt, P., Royston, M. G., Kenward, A. M., Wood, J. R., Carpenter, Multiple imputation for missing data in epidemiological and clinical research: Potential and pitfalls. Br. Med. J., 338 (2009), p. b2393. doi: 10.1136/bmj.b2393
- D. B., Rubin, Multiple Imputation for Non-Response in Surveys, New York: Wiley 1987.
- R., Harel, X. H., Zhou, Multiple imputation: Review of theory, implementation and software. Stat. Med., 26 (2007), 3057–3077. doi: 10.1002/sim.2787
- J. L., Schafer, J. W., Graham, Missing data: Our view of the state of the art. Psychol. Methods, 7 (2002), 147–177. doi: 10.1037/1082-989X.7.2.147
- R Project, R Statistical Computing and Graphics Software: Release 2.12. 2010, Available at http://www.r-project.org/
- SAS Institute Inc, SAS Statistical Analysis Software: Release 9.2, Cary, NC 2010
- J.L. Schafer, MI Software; 1999. Available at http://www.stat.psu.edu/~jls/misoftwa.html
- StataCorp, Stata Statistical Software: Release 11, College Station, TX: Stata Press 2009.
- H., Demirtas, S. A., Freels, R. M., Yucel, Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: A simulation assessment. J. Statist. Comput. Simul., 78(1) (2008), 69–84. doi: 10.1080/10629360600903866
- J. W., Graham, J. L., Schafer, On the performance of multiple imputation for multivariate data with small sample size. In HoyleR. (Ed.), Statistical Strategies for Small Sample Research, (pp. 1–29). Thousand Oaks, CA: Sage 1999.
- J. L., Schafer, Analysis of Incomplete Multivariate Data, Boca Raton, FL: Chapman & Hall/CRC 1997.
- L. W., Li, Y., Conwell, Effects of changes in depressive symptoms and cognitive functioning on physical disability in home care elders. J. Gerontol. A Biol. Sci. Med. Sci., 64A (2009), 230–236. doi: 10.1093/gerona/gln023
- N. J., Wiles, A. M., Haase, J., Gallacher, D. A., Lawlor, G., Lewis, Physical activity and common mental disorder: Results from the caerphilly study. Am. J. Epidemiol., 165(8) (2007), 946–954. doi: 10.1093/aje/kwk070
- C. A., Bernaards, T. R., Belin, J. L., Schafer (2007). Robustness of a multivariate normal approximation for imputation of incomplete binary data. Stat. Med., 26, 1368–1382. doi: 10.1002/sim.2619
- H., Demirtas, Rounding strategies for multiply imputed binary data. Biometric. J., 51(4) (2009), 677–688. doi: 10.1002/bimj.200900018
- H., Demirtas, A distance-based rounding strategy for post-imputation ordinal data. J. Appl. Stat., 37 (2010), 489–500. doi: 10.1080/02664760902744954
- H., Demirtas, D., Hedeker, Comment on Yucel et al. (2008) and reply. Am. Statist., 62 (2008), 364–365. doi: 10.1198/000313008X371752
- N. J., Horton, S. R., Lipsitz, M., Parzen, A potential for bias when rounding in multiple imputation. Am. Statist., 57 (2003), 229–232. doi: 10.1198/0003130032314
- R. M., Yucel, Y., He, A. M., Zaslavsky, Using calibration to improve rounding in imputation. Am. Statist., 62 (2008), 125–129. doi: 10.1198/000313008X300912
- P. D., Allison, Missing Data, Thousand Oaks, CA: Sage 2001.
- Maplesoft, , Maple Mathematical Software: Release 9.5, Ontario: Waterloo. 2007
- L. M., Collins, J. L., Schafer, C., Kam, A comparison of inclusive and restrictive strategies in modern missing data procedures. Psychol. Methods, 6 (2001), 330–351. doi: 10.1037/1082-989X.6.4.330