Multiple Imputation by Chained Equations (MICE): Implementation in Stata
By Patrick Royston, Ian R. White. 2011
1,233 citations recorded by the source corpus.
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.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/fe310066b14cd54a10f99a9b3e5091fe2fed59b7
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Statistics and Probability
- Karin G. M. Groothuis-Oudshoorn
- S. van Buuren
- N. Sarkisian
- D. Fancourt
- N. Gerstel
- E. Paul
- Andrew Steptoe
- Wentao Bao
Citations in the corpus
- Attitudes towards vaccines and intention to vaccinate against COVID-19: Implications for public health communications (2020, positive): “Multiple imputation by chained equations [22] was used to generate 50 imputed datasets for each variable in participants with complete data on all vaccine attitudes and intent (N = 32,361).”
- MICE: Multivariate Imputation by Chained Equations in R (2011, methodology): “The ice software (Royston 2004, 2005; Royston and White 2011) is a widely used implementation in Stata.”
- Does singlehood isolate or integrate? Examining the link between marital status and ties to kin, friends, and neighbors (2016, methodology): “In the GSS, we performed multiple imputation by chained equations (Royston & White, 2011), subsequently deleting the imputed values of the dependent variables (von Hippel, 2007).”
- How to handle missing data: A comparison of different approaches (2015, neutral): “Missing values are imputed based on the assumption that closely similar cases provide the best information for incomplete data (Royston & White, 2011).”
- Bootstrap Inference When Using Multiple Imputation (2016, neutral): “[?̂?lower; ?̂?upper]BMI = [?̂?∗,α BMI; ?̂? ∗,1−α BMI], (5)”