Title | Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts. |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Todem, David, Kyungmann Kim, Jason Fine, and Limin Peng |
Journal | Stat Neerl |
Volume | 64 |
Issue | 2 |
Pagination | 133-156 |
Date Published | 2010 May 01 |
ISSN | 0039-0402 |
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. |
DOI | 10.1111/j.1467-9574.2009.00435.x |
Alternate Journal | Stat Neerl |
Original Publication | Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts. |
PubMed ID | 21258610 |
PubMed Central ID | PMC3023945 |
Grant List | K01 CA131259 / CA / NCI NIH HHS / United States P01 CA142538-01 / CA / NCI NIH HHS / United States R01 CA094893 / CA / NCI NIH HHS / United States R01 CA094893-07 / CA / NCI NIH HHS / United States P01 CA142538 / CA / NCI NIH HHS / United States K01 CA131259-04 / CA / NCI NIH HHS / United States |
Semiparametric regression models and sensitivity analysis of longitudinal data with nonrandom dropouts.
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