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Multiple Imputation for Non-Random Missing Data in Longitudinal Studies of Health-Related Quality of Life

  • Diane L. Fairclough

Abstract

Missing health-related quality of life (QOL) data in longitudinal studies rarely occur for a single reason. Some individuals drop out of the study as a result of mortality or morbidity of the disease or its treatment, others because their disease symptoms have disappeared. Individuals may have intermittent missing assessments for reasons both unrelated and related to treatment. Most methods for analysis focus on a single mechanism, such as informative dropout, and do not address multiple causes and patterns of missing data. These methods are generally based on strong assumptions that cannot be tested because of the missing data. Further, the estimates are often sensitive to the assumptions underlying the methods. The concepts of multiple imputation can be extended to longitudinal studies of QOL allowing 1) the incorporation of a mixture of models reflecting the variety of causes of missing data that occurs in these studies and 2) the uncertainty associated with the underlying assumptions of the models. However, the assumptions underlying the methods for imputing the missing data should be carefully examined in settings where the missing assessments are nonignorable.

Keywords

Propensity Score Multiple Imputation Imputation Model Missing Observation Miss Data Mechanism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media Dordrecht 2002

Authors and Affiliations

  • Diane L. Fairclough
    • 1
  1. 1.AMC Cancer Research CenterUSA

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