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

J Clin Epidemiol

Department of Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, De Boelelaan 1089a, 1081 HV, The Netherlands; EMGO Institute for Health and Care Research, VU University Medical Center, Van der Boechorststraat 7, 1081 BT Amsterdam, The Netherlands; Department of Methodology and Applied Biostatistics, Faculty of Earth and Life Sciences, Institute for Health Sciences, VU University, De Boelelaan 1085, 1081 HV Amsterdam, The Netherlands.

Published: June 2015

AI Article Synopsis

  • Previous research indicates that it's best to handle missing values in questionnaires at the item score level, and this study introduces two new methods to address incomplete item scores in longitudinal studies.
  • The methods utilize item scores or summaries of available scores as auxiliary variables while analyzing the total score of the questionnaire within a latent growth model, enhancing the estimation of incomplete total scores.
  • Results show that including item information leads to more accurate regression coefficient estimates and standard errors, making the use of a parcel summary a recommended approach for longitudinal studies with missing data in multi-item questionnaires.

Article Abstract

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.

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http://dx.doi.org/10.1016/j.jclinepi.2015.01.012DOI Listing

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