[en] The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI method can be classified into two broad classes, the joint modeling and the fully conditional specification approaches. Their relative performance for the longitudinal ordinal data setting under the missing at random (MAR) assumption is not well documented. This paper intends to fill this gap by conducting a large simulation study on the estimation of the parameters of a longitudinal proportional odds model. The two MI methods are also illustrated in quality of life data from a cancer clinical trial.
Disciplines :
Mathematics
Author, co-author :
Donneau, Anne-Françoise ; Université de Liège - ULiège > Département des sciences de la santé publique > Informatique médicale et biostatistique
Mauer, Murielle
Lambert, Philippe ; Université de Liège - ULiège > Institut des sciences humaines et sociales > Méthodes quantitatives en sciences sociales
Molenberghs, Geert
Albert, Adelin ; Université de Liège - ULiège > Département des sciences de la santé publique > Département des sciences de la santé publique
Language :
English
Title :
Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings
Publication date :
2015
Journal title :
Journal of Biopharmaceutical Statistics
ISSN :
1054-3406
eISSN :
1520-5711
Publisher :
Taylor & Francis, Philadelphia, United States - Pennsylvania
Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., Filiberti, A., Flechtner, H., Fleishman, D. B., De Haes, J. C. J. M., Kaasa, S., Klee, M., Osoba, D., Razavi, D., Rofe, P., Schraub, S., Sneeuw, K., Sullivan, M., Takeda, F. (1993). The European organization for research and treatment of cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute 85:365-376.
Ake, C. (2005). Rounding after multiple imputation with non-binary categorical covariates. Paper presented at SAS Users Group International 2005. Thirtieth Annual Conference, Philadelphia.
Allison, P. (2005). Imputation of categorical variables with PROC MI. Paper presented at SAS Users Group international 2005. Thirtieth Annual Conference, Philadelphia.
Bernaards, C., Belin, T., Schafer, J. (2007). Robustness of a multivariate normal approximation for imputation of incomplete binary data. Statistics in Medicine 26:1368-82.
Beunckens C. Sotto C. Molenberghs , G. 2008 A simulation study comparing weighted estimating equations with multiple imputation based estimating equations for longitudinal binary data. Computational Statistics and Data Analysis 52 1533-1548.
Car penter, J., Kenward, M., White, I. (2007). Sensitivity analysis after multiple imputation under missing at random: A weighting approach. Statistical Methods in Medical Research 16:259-275.
Carpenter, J. R., Kenward, M. G. (2007). Missing data in randomised controlled trials:-A practical guide Birmingham: National Institute for Health Research, 1-206. Available at www. missingdata.org.uk.
Demirtas, H., Fr eels, S., Yucel, R. (2008). Plausibility of multivariate normality assumption when multiply imputing non-Gaussian continuous outcomes: A simulation assessment. Journal of Statistical Computation and Simulation 78:69-84.
Donneau, A. F., Mauer, M., Molenberghs, G., Albert, A. (2012). A simulation study comparing multiple imputation methods for incomplete longitudinal ordinal data. Communications in Statistics-Simulation and Computation 2013 (To ap pear
Goodnight, J. H. (1979). A Tutorial on the SWEEP Operator. The American Statistician 33:149-158.
Graham, J. W., Olchowski, A. E., Gilreath, T. D. (2007). How many imputations are really needed? Some practical clarifications of multiple imputation theory Prevention Science 8:206-213.
Horton, N., Lipsitz, S., Parzen, M. (2003). A potential for bias when rounding in multiple imputation. The American Statistician 57:229-232.
Ibrahim, N., Suliadi, S. (2011). Generating correlated discrete ordinal data using R and SAS IML. Computer Methods and Programs in Biomedicine 104(3):122-132.
Lee, A. J. (1997). Some simple methods for generating correlated categorical variates. Computational Statistics and Data Analysis 26:133-148.
Lee, K., Carlin, J. (2010). Multiple imputation for missing data: Fully conditional specification versus multivariate normal imputation. American Journal of Epidemiology 71:624-632.
Liang, K.-Y., Zeger , S. L. (1986). Longitudinal data analysis using generalized linear models. Biometrika 73:13-22.
Lipsitz, S. R., Kim, K., Zhao, L. (1994). Analysis of repeated categorical data using generalized estimating equations. Statistics in Medicine 13(11):1149-1163.
Little, R. J. A., Rubin, D. B. (1987). Statistical Analysis with Missing Data. Wiley: New York.
McCullagh, P. (1980). Regression models for ordinal data (with discussion). Journal of the Royal Statistical Society, Series B 42:109-142.
Olschewski, M., Schulgen, G., Schumacher, M., Altman, D. G. (1994). Quality of life assessment in clinical cancer research. British Journal of Cancer 70:1-5.
Robins, J. M., Rotnitzky, A. (1995). Semiparametric efficiency in multivariate regression models with missing data. Journal of the American Statistical Association 90:122-129.
Robins, J. M., Rotnitzky, A., Zhao, L. (1995). Analysis of semiparametric regression models with missing data. Journal of the American Statistical Association 90:106-121.
Rubin, D. B. (1978). Multiple imputation in sample surveys-A phenomenological Bayesian approach to nonresponse. In: Imputation and Editing of Faulty or Missing Survey Data. Washington, DC: U.S. Department of Commerce, pp. 1-23.
Rubin, D. B. (1987). Multiple Imputations for Nonresponse in Survey. Wiley: New York.
Rubin, D. B. (1976). Inference and missing data. Biometrika 63:581-592.
Schafer, J. L. (1997). Analysis of incomplete multivariate data. Multiple Imputation for Nonresponse in Survey. London: Chapman & Hall.
Stupp, R., M ason, W. P., Van den Bent, M. J., et al. (2005). Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. New England Journal of Medicine 352(10):987-996.
Tanner, M. A., Wong, W. H. (1987). The calculation of posterior distribution by data augmentation. Journal of American Statistical Association 82: 528-550.
Taphoorn, M. J., Stupp, R., Coens, C. , et al. (2005). Health-related quality of life in patients with glioblastoma: A randomized controlled trial. Lancet Oncology 6(12):937-944.
Van Buuren, S. (2007). Multiple imputation of discrete and continuous data by full conditional specification. Statistical Methods in Medical Research 16:219-242.
White, Ian R., Royston, Patrick, Wood, Angela M. (2011). Multiple imputation using chained equations: Issues and guidance for practice. Statistics in Medicine 30(4):377-399.
Williamson, J., Lipsitz, S., Kim, K. (1999). GEECAT and GEEGOR: Computer programs for the analysis of correlated categorical respo nse data. Computer Methods and Programs in Biomedicine 58:25-34.
