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


METHODS
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.


CONCLUSIONS
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.
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.
Iris
I
Eekhout
Department of Psychology, Arizona State University, Box 871104 Tempe AZ 85287-1104, USA.
Craig K
CK
Enders
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.
Jos W R
JW
Twisk
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.
Michiel R
MR
de Boer
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.
Henrica C W
HC
de Vet
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.
Martijn W
MW
Heymans
D003627:Data Interpretation, Statistical; D016013:Likelihood Functions; D008137:Longitudinal Studies; D015233:Models, Statistical; D016032:Randomized Controlled Trials as Topic / Q000295:instrumentation / Q000379:methods; D012107:Research Design; D058028:Research Report / Q000592:standards*; D011795:Surveys and Questionnaires
Auxiliary variables; Full information maximum likelihood; Latent growth modeling; Longitudinal data; Methods; Missing data; Multi-item questionnaire; Structural equation modeling