Multiple Imputation by Chained Equations (MICE): Implementation in Stata. Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors studies in medicine, multiple imputation is becoming the standard route to estimating models with missing covariate data under a missing-at-random assumption. We describe ice, an implementation in Stata of the MICE approach to multiple imputation. Real data from an observational study in ovarian cancer are used to illustrate the most important of the many options available with ice. We remark briefly on the new database architecture and procedures for multiple imputation introduced in releases 11 and 12 of Stata.

References in zbMATH (referenced in 13 articles , 1 standard article )

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  1. Geraci, Marco; McLain, Alexander: Multiple imputation for bounded variables (2018)
  2. Coley, Rebekah Levine; Votruba-Drzal, Elizabeth; Collins, Melissa; Cook, Kyle DeMeo: Comparing public, private, and informal preschool programs in a national sample of low-income children (2016) MathEduc
  3. Galindo, Claudia; Sonnenschein, Susan: Decreasing the SES math achievement gap: initial math proficiency and home learning environments (2015) MathEduc
  4. Grover, Gurprit; Gupta, Vinay K.: Multiple imputation of censored survival data in the presence of missing covariates using restricted mean survival time (2015)
  5. Mitani, Aya A.; Kurian, Allison W.; Das, Amar K.; Desai, Manisha: Navigating choices when applying multiple imputation in the presence of multi-level categorical interaction effects (2015)
  6. Seaman, Shaun R.; White, Ian R.; Copas, Andrew J.; Li, Leah: Combining multiple imputation and inverse-probability weighting (2012)
  7. Drechsler, Jörg: Multiple imputation in practice -- a case study using a complex German establishment survey (2011)
  8. Patrick Royston; Ian White: Multiple Imputation by Chained Equations (MICE): Implementation in Stata (2011) not zbMATH
  9. Recai Yucel: State of the Multiple Imputation Software (2011) not zbMATH
  10. Stef van Buuren; Karin Groothuis-Oudshoorn: mice: Multivariate Imputation by Chained Equations in R (2011) not zbMATH
  11. White, Ian R.; Daniel, Rhian; Royston, Patrick: Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables (2010)
  12. Juned Siddique; Ofer Harel: MIDAS: A SAS Macro for Multiple Imputation Using Distance-Aided Selection of Donors (2009) not zbMATH
  13. Patrick Royston: Multiple imputation of missing values: Update of ice (2005) not zbMATH