Open this publication in new window or tab >>2013 (English)In: Journal of Business Administration Research, ISSN 1927-9507, E-ISSN 1927-9515, Vol. 2, no 1, p. Article ID: 1-Article in journal (Refereed) Published
Abstract [en]
Experimental studies often measure an individual’s quality of life before and after an intervention, with the data organized into a square table and analyzed using matched pair modeling. However, it is not unusual to find missing data in either round (i.e., before and/or after) of such studies and the use of multiple imputations with matched-pair modeling remains relatively unreported in the applied statistics literature. In this paper we introduce an approach which maintains dependency of responses over time and makes a match between the imputer and the analyst. We use ‘before’ and ‘after’ quality-of-life data from a randomized controlled trial to demonstrate how multiple imputation and matched-pair modeling can be congenially combined, avoiding a possible mismatch of imputation and analyses, and to derive a properly consolidated analysis of the quality-of-life data. We illustrate this strategy with a real-life example of one item from a quality-of-life study that evaluates the effectiveness of patients’ self-management of anticoagulation versus standard care as part of a randomized controlled trial.
National Category
Economics and Business
Research subject
Statistics/Econometrics
Identifiers
urn:nbn:se:lnu:diva-23181 (URN)10.5430/jbar.v2n1p1 (DOI)
2012-12-212012-12-212023-11-24Bibliographically approved