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Year: 2011

  • White, I. R., Royston, P. & Wood, A. M. (2011). Multiple imputation using chained equations: issues and guidance for practice. Statistics in Medicine, 30, 377-399. [More] [Online version] [Bibtex]

Year: 2010

  • Arciniegas-Alarcón, S., García-Peña, M., dos Santos Dias, C. T. & Krzanowski, W. J. (2010). An alternative methodology for imputing missing data in trials with genotype-by-environment interaction. Biometrical Letters, 47(1), 1-14. [More] [Online version] [Bibtex]
  • Shaibani, A., Fares, S., Selam, J. L., Arslanian, A., Simpson, J., Sen, D. et al. (2010). LOCF Approach to Handling Missing Data Overestimates the Pain Score Improvement of Drop-Outs Reply. Journal of Pain, 11(5), 502-503. [More] [Bibtex]
  • Shin, Y. Y. & Raudenbush, S. W. (2010). A Latent Cluster-Mean Approach to the Contextual Effects Model With Missing Data. Journal of Educational and Behavioral Statistics, 35(1), 26-53. [More] [Bibtex]
  • Shortreed, S. M. & Forbes, A. B. (2010). Missing data in the exposure of interest and marginal structural models: A simulation study based on the Framingham Heart Study. Statistics In Medicine, 29(4), 431-443. [More] [Bibtex]
  • Shutoh, N., Kusumi, M., Morinaga, W., Yamada, S. & Seo, T. (2010). Testing Equality of Mean Vectors in Two Sample Problem with Missing Data. Communications In Statistics-simulation and Computation, 39(3), 487-500. [More] [Bibtex]
  • Sinha, S. (2010). An estimated-score approach for dealing with missing covariate data in matched case-control studies. Canadian Journal of Statistics-revue Canadienne De Statistique, 38(4), 680-697. [More] [Bibtex]
  • Smolkowski, K., Danaher, B. G., Seeley, J. R., Kosty, D. B. & Severson, H. H. (2010). Modeling missing binary outcome data in a successful web-based smokeless tobacco cessation program. Addiction, 105(6), 1005-1015. [More] [Bibtex]
  • Soullier, N., de La Rochebrochard, E. & Bouyer, J. (2010). Multiple imputation for estimation of an occurrence rate in cohorts with attrition and discrete follow-up time points: a simulation study. Bmc Medical Research Methodology, 10. [More] [Bibtex]
  • Spratt, M., Carpenter, J., Sterne, J. A., Carlin, J. B., Heron, J., Henderson, J. et al. (2010). Strategies for Multiple Imputation in Longitudinal Studies. American Journal of Epidemiology, 172(4), 478-487. [More] [Bibtex]
  • Vergouwe, Y., Royston, P., Moons, K. G. & Altman, D. G. (2010). Development and validation of a prediction model with missing predictor data: a practical approach.. J Clin Epidemiol, 63(2), 205-14. [More] [Bibtex]
  • Vergouwe, Y., Royston, P., Moons, K. G. & Altman, D. G. (2010). Development and validation of a prediction model with missing predictor data: a practical approach. Journal of Clinical Epidemiology, 63(2), 205-214. [More] [Bibtex]
  • Wagner, B. & Smith, T. S. (2010). Missing data analyses.. J Am Coll Surg, 211(3), 435; author reply 43. [More] [Bibtex]
  • Wallace, M. L., Anderson, S. J. & Mazumdar, S. (2010). A stochastic multiple imputation algorithm for missing covariate data in tree-structured survival analysis. Statistics In Medicine, 29(29), 3004-3016. [More] [Bibtex]
  • Wang, C. L. & Hall, C. B. (2010). Correction of bias from non-random missing longitudinal data using auxiliary information. Statistics In Medicine, 29(6), 671-679. [More] [Bibtex]
  • White, I. R. & Carlin, J. B. (2010). Bias and efficiency of multiple imputation compared with complete-case analysis for missing covariate values. Statistics In Medicine, 29(28), 2920-2931. [More] [Bibtex]
  • White, I. R., Daniel, R. & Royston, P. (2010). Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables. Computational Statistics & Data Analysis, 54(10), 2267-2275. [More] [Bibtex]
  • Wirth, K. E., Tchetgen, E. J. & Murray, M. (2010). Adjustment for Missing Data in Complex Surveys Using Doubly Robust Estimation Application to Commercial Sexual Contact Among Indian Men. Epidemiology, 21(6), 863-871. [More] [Bibtex]
  • Xu, L. Z. & Zhang, J. J. (2010). Multiple imputation method for the semiparametric accelerated failure time mixture cure model. Computational Statistics & Data Analysis, 54(7), 1808-1816. [More] [Bibtex]
  • Yang, Y. & Kang, J. (2010). Joint analysis of mixed Poisson and continuous longitudinal data with nonignorable missing values. Computational Statistics & Data Analysis, 54(1), 193-207. [More] [Bibtex]
  • Yoo, B. (2010). Impact of missing data on type 1 error rates in non-inferiority trials. Pharmaceutical Statistics, 9(2), 87-99. [More] [Bibtex]
  • Yu, B. B., Saczynski, J. S. & Launer, L. (2010). Multiple imputation for estimating the risk of developing dementia and its impact on survival. Biometrical Journal, 52(5), 616-627. [More] [Bibtex]
  • Yuan, K. H. & Bentler, P. M. (2010). Consistency of Normal-Distribution-Based Pseudo Maximum Likelihood Estimates When Data Are Missing at Random. American Statistician, 64(3), 263-267. [More] [Bibtex]
  • Yuan, Y. & Yin, G. S. (2010). Bayesian Quantile Regression for Longitudinal Studies with Nonignorable Missing Data. Biometrics, 66(1), 105-114. [More] [Bibtex]
  • Yucel, R. M. & Demirtas, H. (2010). Impact of non-normal random effects on inference by multiple imputation: A simulation assessment. Computational Statistics & Data Analysis, 54(3), 790-801. [More] [Bibtex]
Results 51 - 75 of 701