Local influence diagnostics for incomplete overdispersed longitudinal counts

Trias Wahyuni Rakhmawati, Geert Molenberghs, Geert Verbeke, Christel Faes
2016 Figshare  
We develop local influence diagnostics to detect influential subjects when generalized linear mixed models are fitted to incomplete longitudinal overdispersed count data. The focus is on the influence stemming from the dropout model specification. In particular, the effect of small perturbations around an MAR specification are examined. The method is applied to data from a longitudinal clinical trial in epileptic patients. The effect on models allowing for overdispersion is contrasted with that
more » ... ontrasted with that on models that do not.
doi:10.6084/m9.figshare.2336983 fatcat:5qx3gxw3ybayzgzrdai5sbgvyy