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Longitudinal data analysis for generalized linear models with follow-up dependent on outcome-related variables. (English) Zbl 1143.62041
Summary: In longitudinal studies, observation times are often irregular and subject-specific. Frequently they are related to the outcome measure or other variables that are associated with the outcome measure but undesirable to condition upon in the model for outcome. Regression analyses that are unadjusted for outcome-dependent follow-up then yield biased estimates.
The authors propose a class of inverse-intensity rate-ratio weighted estimators in generalized linear models that adjust for outcome-dependent follow-up. The estimators, based on estimating equations, are very simple and easily computed; they can be used under mixtures of continuous and discrete observation times. The predictors of observation times can be past observed outcomes, cumulative values of outcome-model covariates and other factors associated with the outcome. The authors validate their approach through simulations and they illustrate it using data from a supported housing program from the US federal government.

MSC:
62J12 Generalized linear models (logistic models)
62N02 Estimation in survival analysis and censored data
62M09 Non-Markovian processes: estimation
Software:
R; Stata
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