Imputation-based strategies for clinical trial longitudinal data with nonignorable missing values

Xiaowei Yang, Jinhui Li, Steven Shoptaw

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


Biomedical research is plagued with problems of missing data, especially in clinical trials of medical and behavioral therapies adopting longitudinal design. After a literature review on modeling incomplete longitudinal data based on full-likelihood functions, this paper proposes a set of imputation-based strategies for implementing selection, pattern-mixture, and shared-parameter models for handling intermittent missing values and dropouts that are potentially nonignorable according to various criteria. Within the framework of multiple partial imputation, intermittent missing values are first imputed several times; then, each partially imputed data set is analyzed to deal with dropouts with or without further imputation. Depending on the choice of imputation model or measurement model, there exist various strategies that can be jointly applied to the same set of data to study the effect of treatment or intervention from multi-faceted perspectives. For illustration, the strategies were applied to a data set with continuous repeated measures from a smoking cessation clinical trial.

Original languageEnglish (US)
Pages (from-to)2826-2849
Number of pages24
JournalStatistics in Medicine
Issue number15
StatePublished - Jul 10 2008


  • Intermittent missing values
  • Markov transition model
  • Multiple partial imputation
  • Nonignorable dropout
  • Pattern-mixture model
  • Selection model

ASJC Scopus subject areas

  • Epidemiology


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