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Investigating the missing data mechanism in quality of life outcomes: a comparison of approaches

By Shona Fielding, Peter M. Fayer and Craig R Ramsay

Abstract

Background: Missing data is classified as missing completely at random (MCAR), missing at\ud random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying\ud the most appropriate analysis. The first aim was to compare different methods for identifying this\ud missing data mechanism to determine if they gave consistent conclusions. Secondly, to investigate\ud whether the reminder-response data can be utilised to help identify the missing data mechanism.\ud Methods: Five clinical trial datasets that employed a reminder system at follow-up were used.\ud Some quality of life questionnaires were initially missing, but later recovered through reminders.\ud Four methods of determining the missing data mechanism were applied. Two response data\ud scenarios were considered. Firstly, immediate data only; secondly, all observed responses\ud (including reminder-response).\ud Results: In three of five trials the hypothesis tests found evidence against the MCAR assumption.\ud Logistic regression suggested MAR, but was able to use the reminder-collected data to highlight\ud potential MNAR data in two trials.\ud Conclusion: The four methods were consistent in determining the missingness mechanism. One\ud hypothesis test was preferred as it is applicable with intermittent missingness. Some inconsistencies between the two data scenarios were found. Ignoring the reminder data could potentially give a distorted view of the missingness mechanism. Utilising reminder data allowed the possibility of MNAR to be considered.The Chief Scientist Office of the Scottish Government Health Directorate. \ud Research Training Fellowship (CZF/1/31

Topics: Data collection, Quality of life, Questionnaires
Publisher: BMC
Year: 2009
OAI identifier: oai:aura.abdn.ac.uk:2164/293
Journal:

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