Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts |
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Authors: | Todem David Kim Kyungmann Fine Jason Peng Limin |
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Institution: | Division of Biostatistics, Department of Epidemiology, Michigan State University, B601 West Fee Hall, East Lansing, MI 48824, U.S.A. |
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Abstract: | We propose a family of regression models to adjust for nonrandom dropouts in the analysis of longitudinal outcomes with fully observed covariates. The approach conceptually focuses on generalized linear models with random effects. A novel formulation of a shared random effects model is presented and shown to provide a dropout selection parameter with a meaningful interpretation. The proposed semiparametric and parametric models are made part of a sensitivity analysis to delineate the range of inferences consistent with observed data. Concerns about model identifiability are addressed by fixing some model parameters to construct functional estimators that are used as the basis of a global sensitivity test for parameter contrasts. Our simulation studies demonstrate a large reduction of bias for the semiparametric model relatively to the parametric model at times where the dropout rate is high or the dropout model is misspecified. The methodology's practical utility is illustrated in a data analysis. |
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Keywords: | exponential family distribution functional estimators global sensitivity analysis informative dropout infimum/ supremum statistic non‐parametric mixture uniform convergence non‐identifiable models |
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