A residuals-based transition model for longitudinal analysis with estimation in the presence of missing data

Tulay Koru-Sengul, David S. Stoffer, Nancy L. Day

Research output: Contribution to journalArticle

8 Scopus citations

Abstract

We propose a transition model for analysing data from complex longitudinal studies. Because missing values are practically unavoidable in large longitudinal studies, we also present a two-stage imputation method for handling general patterns of missing values on both the outcome and the covariates by combining multiple imputation with stochastic regression imputation. Our model is a time-varying autoregression on the past innovations (residuals), and it can be used in cases where general dynamics must be taken into account, and where the model selection is important. The entire estimation process was carried out using available procedures in statistical packages such as SAS and S-PLUS. To illustrate the viability of the proposed model and the two-stage imputation method, we analyse data collected in an epidemiological study that focused on various factors relating to childhood growth. Finally, we present a simulation study to investigate the behaviour of our two-stage imputation procedure.

Original languageEnglish (US)
Pages (from-to)3330-3341
Number of pages12
JournalStatistics in Medicine
Volume26
Issue number17
DOIs
StatePublished - Jul 30 2007
Externally publishedYes

    Fingerprint

Keywords

  • Incomplete data
  • Innovations sequence
  • Longitudinal analysis
  • Missing data
  • Multiple imputation
  • Stochastic regression imputation
  • Time-varying autoregression

ASJC Scopus subject areas

  • Epidemiology

Cite this