title: Passive imputation and parcel summaries are both valid to handle missing items in studies with many multi-item scales.
abstract: Previous studies showed that missing data in multi-item scales can best be handled by multiple imputation of item scores. However, when many scales are used, the number of items will become too large for the imputation model to reliably estimate imputations. A solution is to use passive imputation or a parcel summary score that combine and consequently reduce the number of variables in the imputation model. The performance of these methods was evaluated in a simulation study and illustrated in an example. Passive imputation, which updated scale scores from imputed items, and parcel summary scores that use the average over available item scores were compared to using all items simultaneously, imputing total scores of scales and complete-case analysis. Scale scores and coefficient estimates from linear regression were compared to "true" parameters on bias and precision. Passive imputation and using parcel summaries showed smaller bias and more precision than imputing total scores and complete-case analyses. Passive imputation or using parcel summary scores are valid missing data solutions in studies that include many multi-item scales.
authors.affiliation: 1 Netherlands Organization for Applied Scientific Research (TNO), Leiden, Netherlands.|2 EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, Netherlands.|3 VU University, Amsterdam, Netherlands.
authors.forename: Iris
authors.initials: I
authors.lastname: Eekhout
authors.affiliation: 2 EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, Netherlands.
authors.forename: Henrica Cw
authors.initials: HC
authors.lastname: de Vet
authors.affiliation: 3 VU University, Amsterdam, Netherlands.
authors.forename: Michiel R
authors.initials: MR
authors.lastname: de Boer
authors.affiliation: 2 EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, Netherlands.
authors.forename: Jos Wr
authors.initials: JW
authors.lastname: Twisk
authors.affiliation: 2 EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, Netherlands.|3 VU University, Amsterdam, Netherlands.
authors.forename: Martijn W
authors.initials: MW
authors.lastname: Heymans
mesh_terms: D015982:Bias; D035843:Biomedical Research; D016014:Linear Models; D017063:Outcome Assessment, Health Care / Q000379:methods / Q000706:statistics & numerical data; D012107:Research Design / Q000706:statistics & numerical data; D011795:Surveys and Questionnaires / Q000706:statistics & numerical data
keywords: Multiple imputation; item imputation; missing data; questionnaires; simulation study