Handling Missing Values in Longitudinal Panel Data With Multiple Imputation
作者： Rebekah Young , David R. Johnson
DOI: 10.1111/JOMF.12144
关键词:
摘要: … of longitudinal panel data in the presence of missing values. … studies, apply several techniques to a simulations study … measures; time-to-event models; nonrandom study dropout; and …
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Jennifer E. V. Lloyd, Jelena Obradović, Richard M. Carpiano, Frosso Motti-Stefanidi, JMASM 32: Multiple Imputation of Missing Multilevel, Longitudinal Data: A Case When Practical Considerations Trump Best Practices? Journal of Modern Applied Statistical Methods. ,vol. 12, pp. 261- 275 ,(2013) , 10.22237/JMASM/1367382480
Catherine Welch, Jonathan Bartlett, Irene Petersen, Application of Multiple Imputation using the Two-Fold Fully Conditional Specification Algorithm in Longitudinal Clinical Data The Stata Journal: Promoting communications on statistics and Stata. ,vol. 14, pp. 418- 431 ,(2014) , 10.1177/1536867X1401400213
Donald R. Hedeker, Robert D. Gibbons, Longitudinal Data Analysis ,(2006)
Craig K. Enders, Analyzing Longitudinal Data With Missing Values Rehabilitation Psychology. ,vol. 56, pp. 267- 288 ,(2011) , 10.1037/A0025579
Helena Jeličić, Erin Phelps, Richard M. Lerner, Use of missing data methods in longitudinal studies: the persistence of bad practices in developmental psychology. Developmental Psychology. ,vol. 45, pp. 1195- 1199 ,(2009) , 10.1037/A0015665
Rebekah Young, David Johnson, Methods for Handling Missing Secondary Respondent Data. Journal of Marriage and Family. ,vol. 75, pp. 221- 234 ,(2013) , 10.1111/J.1741-3737.2012.01021.X
Nicholas J Horton, Stuart R Lipsitz, Michael Parzen, A Potential for Bias When Rounding in Multiple Imputation The American Statistician. ,vol. 57, pp. 229- 232 ,(2003) , 10.1198/0003130032314
Roderick J. Little, Ralph D'Agostino, Michael L. Cohen, Kay Dickersin, Scott S. Emerson, John T. Farrar, Constantine Frangakis, Joseph W. Hogan, Geert Molenberghs, Susan A. Murphy, James D. Neaton, Andrea Rotnitzky, Daniel Scharfstein, Weichung J. Shih, Jay P. Siegel, Hal Stern, The Prevention and Treatment of Missing Data in Clinical Trials The New England Journal of Medicine. ,vol. 367, pp. 1355- 1360 ,(2012) , 10.1056/NEJMSR1203730
Roderick J. A. Little, Modeling the Drop-Out Mechanism in Repeated-Measures Studies Journal of the American Statistical Association. ,vol. 90, pp. 1112- 1121 ,(1995) , 10.1080/01621459.1995.10476615
David R. Johnson, Rebekah Young, Toward Best Practices in Analyzing Datasets with Missing Data: Comparisons and Recommendations Journal of Marriage and Family. ,vol. 73, pp. 926- 945 ,(2011) , 10.1111/J.1741-3737.2011.00861.X