Multiple Imputation for Multilevel Data with Continuous and Binary Variables

Vincent Audigier, Ian R. White, Shahab Jolani, Thomas P. A. Debray, Matteo Quartagno, James Carpenter, Stef van Buuren, Matthieu Resche-Rigon

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Pages (from-to)160-183
Number of pages24
JournalStatistical Science
Volume33
Issue number2
DOIs
Publication statusPublished - 1 May 2018

Keywords

  • Missing data
  • systematically missing values
  • multilevel data
  • mixed data
  • multiple imputation
  • joint modelling
  • fully conditional specification
  • FULLY CONDITIONAL SPECIFICATION
  • INDIVIDUAL PATIENT DATA
  • INTEGRATIVE DATA-ANALYSIS
  • MIXED-EFFECTS MODELS
  • MISSING-DATA
  • CHAINED EQUATIONS
  • MULTIVARIATE IMPUTATION
  • OBSERVATIONAL COHORT
  • RANDOMIZED-TRIALS
  • BAYESIAN-APPROACH

Cite this

Audigier, V., White, I. R., Jolani, S., Debray, T. P. A., Quartagno, M., Carpenter, J., van Buuren, S., & Resche-Rigon, M. (2018). Multiple Imputation for Multilevel Data with Continuous and Binary Variables. Statistical Science, 33(2), 160-183. https://doi.org/10.1214/18-STS646