Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings

J Biopharm Stat. 2015;25(3):570-601. doi: 10.1080/10543406.2014.920864.


The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI method can be classified into two broad classes, the joint modeling and the fully conditional specification approaches. Their relative performance for the longitudinal ordinal data setting under the missing at random (MAR) assumption is not well documented. This article intends to fill this gap by conducting a large simulation study on the estimation of the parameters of a longitudinal proportional odds model. The two MI methods are also illustrated in quality of life data from a cancer clinical trial.

Keywords: Intermittent missingness; Longitudinal analysis; Missing at random; Multiple imputation; Non-monotone missingness; Ordinal variables.

MeSH terms

  • Central Nervous System Neoplasms / drug therapy
  • Central Nervous System Neoplasms / radiotherapy
  • Computer Simulation
  • Glioblastoma / drug therapy
  • Glioblastoma / radiotherapy
  • Humans
  • Logistic Models
  • Longitudinal Studies*
  • Models, Statistical*
  • Multivariate Analysis
  • Patient Dropouts* / statistics & numerical data
  • Quality of Life
  • Randomized Controlled Trials as Topic / methods
  • Randomized Controlled Trials as Topic / statistics & numerical data*
  • Statistical Distributions
  • Surveys and Questionnaires