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Carpenter, J. R., Goldstein, H. & Kenward, M. G. (2011). REALCOM-IMPUTE Software for Multilevel Multiple Imputation with Mixed Response Types. Journal of Statistical Software, 45(5).[More][Online version][Bibtex]
Akacha, M. & Hutton, J. L. (2011). Modelling the rate of change in a longitudinal study with missing
data, adjusting for contact attempts. Statistics In Medicine, 30(10), 1072-1089.[More][Bibtex]
Ali, A. M., Dawson, S. J., Blows, F. M., Provenzano, E., Ellis, I. O., Baglietto, L. et al. (2011). Comparison of methods for handling missing data on immunohistochemical
markers in survival analysis of breast cancer. British Journal of Cancer, 104(4), 693-699.[More][Bibtex]
Andridge, R. R. (2011). Quantifying the impact of fixed effects modeling of clusters in multiple
imputation for cluster randomized trials. Biometrical Journal, 53(1), 57-74.[More][Bibtex]
Azur, M. J., Stuart, E. A., Frangakis, C. & Leaf, P. J. (2011). Multiple imputation by chained equations: what is it and how does
it work?. International Journal of Methods In Psychiatric Research, 20(1), 40-49.[More][Bibtex]
Berger, V. W. & Vali, B. (2011). Intent-to-Randomize Corrections for Missing Data Resulting from Run-In
Selection Bias in Clinical Trials for Chronic Conditions. Journal of Biopharmaceutical Statistics, 21(2), 263-270.[More][Bibtex]
Birhanu, T., Molenberghs, G., Sotto, C. & Kenward, M. G. (2011). Doubly Robust and Multiple-Imputation-Based Generalized Estimating
Equations. Journal of Biopharmaceutical Statistics, 21(2), 202-225.[More][Bibtex]
Burns, R. A., Butterworth, P., Kiely, K. M., Bielak, A. A., Luszcz, M. A., Mitchell, P. et al. (2011). Multiple imputation was an efficient method for harmonizing the Mini-Mental
State Examination with missing item-level data. Journal of Clinical Epidemiology, 64(7), 787-793.[More][Bibtex]
Campbell, G., Pennello, G. & Yue, L. (2011). Missing Data in the Regulation of Medical Devices. Journal of Biopharmaceutical Statistics, 21(2), 180-195.[More][Bibtex]
Dinh, P. & Yang, P. L. (2011). Handling Baselines in Repeated Measures Analyses with Missing Data
at Random. Journal of Biopharmaceutical Statistics, 21(2), 326-341.[More][Bibtex]
Enders, C. K. & Gottschall, A. C. (2011). Multiple Imputation Strategies for Multiple Group Structural Equation
Models. Structural Equation Modeling-a Multidisciplinary Journal, 18(1), 35-54.[More][Bibtex]
Fleming, T. R. (2011). Addressing Missing Data in Clinical Trials. Annals of Internal Medicine, 154(2), 113-+.[More][Bibtex]
Gower, K. (2011). Missing Data: A Gentle Introduction. Organizational Research Methods, 14(3), 577-580.[More][Bibtex]
Graber-Naidich, A., Gorfine, M., Malone, K. E. & Hsu, L. (2011). Missing genetic information in case-control family data with general
semi-parametric shared frailty model. Lifetime Data Analysis, 17(2), 175-194.[More][Bibtex]
Graham, B. S. & Hirano, K. (2011). Robustness to Parametric Assumptions in Missing Data Models. American Economic Review, 101(3), 538-543.[More][Bibtex]
He, W. Q. & Yi, G. Y. (2011). A Pairwise Likelihood Method For Correlated Binary Data With/without
Missing Observations Under Generalized Partially Linear Single-index
Models. Statistica Sinica, 21(1), 207-229.[More][Bibtex]
He, Y. L., Yucel, R. & Raghunathan, T. E. (2011). A functional multiple imputation approach to incomplete longitudinal
data. Statistics In Medicine, 30(10), 1137-1156.[More][Bibtex]
Helms, R. W. & Reece, L. H. (2011). Defining, Evaluating, and Removing Bias Induced by Linear Imputation
in Longitudinal Clinical Trials with MNAR Missing Data. Journal of Biopharmaceutical Statistics, 21(2), 226-251.[More][Bibtex]
Jenkins, S. P., Burkhauser, R. V., Feng, S. Z. & Larrimore, J. (2011). Measuring inequality using censored data: a multiple-imputation approach
to estimation and inference. Journal of the Royal Statistical Society Series A-statistics In Society, 174, 63-81.[More][Bibtex]
Keene, O. N. (2011). Intent-to-treat analysis in the presence of off-treatment or missing
data. Pharmaceutical Statistics, 10(3), 191-195.[More][Bibtex]
Kim, J. K. (2011). Parametric fractional imputation for missing data analysis. Biometrika, 98(1), 119-132.[More][Bibtex]
Kim, J. K. & Yu, C. L. (2011). A Semiparametric Estimation of Mean Functionals With Nonignorable
Missing Data. Journal of the American Statistical Association, 106(493), 157-165.[More][Bibtex]
Kim, Y. (2011). Missing Data Handling in Chronic Pain Trials. Journal of Biopharmaceutical Statistics, 21(2), 311-325.[More][Bibtex]
Li, X. M., Wang, W. W., Liu, G. H. & Chan, I. S. (2011). Handling Missing Data in Vaccine Clinical Trials for Immunogenicity
and Safety Evaluation. Journal of Biopharmaceutical Statistics, 21(2), 294-310.[More][Bibtex]
Liu, G. F. & Zhan, X. J. (2011). Comparisons of Methods for Analysis of Repeated Binary Responses
with Missing Data. Journal of Biopharmaceutical Statistics, 21(3), 371-392.[More][Bibtex]
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