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

Shona Fielding* (Corresponding Author), Peter M Fayers, Craig R Ramsay

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

53 Citations (Scopus)
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Background: Missing data is classified as missing completely at random (MCAR), missing at random (MAR) or missing not at random (MNAR). Knowing the mechanism is useful in identifying the most appropriate analysis. The first aim was to compare different methods for identifying this missing data mechanism to determine if they gave consistent conclusions. Secondly, to investigate whether the reminder-response data can be utilised to help identify the missing data mechanism.

Methods: Five clinical trial datasets that employed a reminder system at follow-up were used. Some quality of life questionnaires were initially missing, but later recovered through reminders. Four methods of determining the missing data mechanism were applied. Two response data scenarios were considered. Firstly, immediate data only; secondly, all observed responses (including reminder-response).

Results: In three of five trials the hypothesis tests found evidence against the MCAR assumption. Logistic regression suggested MAR, but was able to use the reminder-collected data to highlight potential MNAR data in two trials.

Conclusion: The four methods were consistent in determining the missingness mechanism. One 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.

Original languageEnglish
Article number57
Number of pages10
JournalHealth and Quality of Life Outcomes
Issue number-
Publication statusPublished - 22 Jun 2009


  • random dropouts
  • clinical-trials
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