Multiple imputation of missing values. .. This article describes five ado-files. mvis creates multiple multivariate imputations. uvis imputes missing values for a single variable as a function of several covariates, each with complete data. micombine fits a wide variety of regression models to a multiply imputed dataset, combining the estimates using Rubin’s rules, and supports survival analysis models (stcox and streg), categorical data models, generalized linear models, and more. Finally, misplit and mijoin are utilities to interconvert datasets created by mvis and by the miset program from John Carlin and colleagues. The use of the routines is illustrated with an example of prognostic modeling in breast cancer.

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  1. Nengsih, Titin Agustin; Bertrand, Frédéric; Maumy-Bertrand, Myriam; Meyer, Nicolas: Determining the number of components in PLS regression on incomplete data set (2019)
  2. Pan, Qiyuan; Wei, Rong: Improved methods for estimating fraction of missing information in multiple imputation (2018)
  3. Kombo, A. Y.; Mwambi, H.; Molenberghs, G.: Multiple imputation for ordinal longitudinal data with monotone missing data patterns (2017)
  4. Dulaney, Alana; Vasilyeva, Marina; O’Dwyer, Laura: Individual differences in cognitive resources and elementary school mathematics achievement: examining the roles of storage and attention (2015) MathEduc
  5. Gao, Hang; Liu, Xin-Wang; Peng, Yu-Xing; Jian, Song-Lei: Sample-based extreme learning machine with missing data (2015)
  6. Ziegelmeyer, Michael: Illuminate the unknown: evaluation of imputation procedures based on the SAVE survey (2013)
  7. Daniel, Rhian M.; Kenward, Michael G.: A method for increasing the robustness of multiple imputation (2012)
  8. White, Ian R.; Daniel, Rhian; Royston, Patrick: Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables (2010)
  9. Mattei, Alessandra: Estimating and using propensity score in presence of missing background data: an application to assess the impact of childbearing on wellbeing (2009)
  10. Paul, Christopher; Mason, William M.; Mccaffrey, Daniel; Fox, Sarah A.: A cautionary case study of approaches to the treatment of missing data (2008)