Format

Send to

Choose Destination
J Clin Epidemiol. 2015 Jun;68(6):637-45. doi: 10.1016/j.jclinepi.2015.01.012. Epub 2015 Jan 28.

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

Author information

1
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. Electronic address: i.eekhout@vumc.nl.
2
Department of Psychology, Arizona State University, Box 871104 Tempe AZ 85287-1104, USA.
3
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.
4
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.
5
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.

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.

KEYWORDS:

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

PMID:
25724894
DOI:
10.1016/j.jclinepi.2015.01.012
[Indexed for MEDLINE]
Free full text

Supplemental Content

Full text links

Icon for Elsevier Science
Loading ...
Support Center