Inverse Intensity Weighting in Generalized Linear Models As an Option for Analyzing Longitudinal Data with Triggered Observations

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 illustrate the methods and benefits of inverse intensity weighting in GLMs. An additional benefit of the analytical approach presented is that it allows for assessment of the utility of triggered sampling in longitudinal studies.