Including auxiliary item information in longitudinal data analyses improved handling missing questionnaire outcome data

Iris Eekhout, Craig K. Enders, Jos W R Twisk, Michiel R. De Boer, Henrica C W De Vet, Martijn W. Heymans

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Objectives Previous studies show that missing values in multi-item questionnaires can best be handled at item score level. The aim of this study was to demonstrate two novel methods for dealing with incomplete item scores in outcome variables in longitudinal studies. The performance of these methods was previously examined in a simulation study. The two methods incorporate item information at the background when simultaneously the study outcomes are estimated. Study Design and Setting The investigated methods include the item scores or a summary of a parcel of available item scores as auxiliary variables while using the total score of the multi-item questionnaire as the main focus of the analysis in a latent growth model. That way the items help estimating the incomplete information of the total scores. The methods are demonstrated in two empirical data sets. Results Including the item information results in more precise outcomes in terms of regression coefficient estimates and standard errors, compared with not including item information in the analysis. Conclusion The inclusion of a parcel summary is an efficient method that does not overcomplicate longitudinal growth estimates. Therefore, it is recommended in situations where multi-item questionnaires are used as outcome measure in longitudinal clinical studies with incomplete scores because of missing item scores.

Original languageEnglish (US)
Pages (from-to)637-645
Number of pages9
JournalJournal of Clinical Epidemiology
Issue number6
StatePublished - Jun 1 2015


  • Auxiliary variables
  • Full information maximum likelihood
  • Latent growth modeling
  • Longitudinal data
  • Methods
  • Missing data
  • Multi-item questionnaire
  • Structural equation modeling

ASJC Scopus subject areas

  • Epidemiology


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