Inverse intensity weighting in generalized linear models as an option for analyzing longitudinal data with triggered observations.

American Journal of Epidemiology
Peter H Van NessHaiqun Lin

Abstract

Longitudinal epidemiologic studies with irregularly observed categorical outcomes present considerable analytical challenges. Generalized linear models (GLMs) tolerate without bias only values missing completely at random and assume that all observations contribute equally. A triggered sampling study design and an analysis using inverse intensity weights in a GLM offer promise of effectively addressing both shortcomings. A triggered sampling design generates irregularly spaced outcomes because, in addition to regularly scheduled follow-up interviews, it specifies that data be collected after a "trigger" (a decline in health status during follow-up) occurs. It is intended to mitigate bias introduced by study participant loss to follow-up. For each observation, an inverse intensity weight is calculated from an Anderson-Gill recurrent-event regression model whose events of interest are observed interviews; the weights help to equalize observation contributions. Investigators in the Longitudinal Examination of Attitudes and Preferences (LEAP) Study (1999-2002), a Connecticut study of seriously ill older adults at the end of life, used a triggered sampling design. In this paper, the authors analyze data from the LEAP Study to illust...Continue Reading

References

Dec 1, 1981·Journal of Health and Social Behavior·L I PearlinJ T Mullan
Jul 1, 1997·Journal of General Internal Medicine·M A WhooleyW S Browner
Apr 5, 2002·The New England Journal of Medicine·Terri R FriedHeather Allore
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Apr 26, 2006·Archives of Internal Medicine·Terri R FriedJoel A Dubin
Feb 13, 2007·Journal of Clinical Epidemiology·Joel A DubinTerri R Fried

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Citations

Jun 11, 2010·Journal of the American Geriatrics Society·Peter H Van NessHeather G Allore
Feb 22, 2013·Scandinavian Journal of Rheumatology·R KnevelA H M van der Helm-van Mil

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