Title 



A sensitivity analysis for sharedparameter models for incomplete longitudinal outcomes
 
Author 



 
Abstract 



All models for incomplete data either explicitly make assumptions about aspects of the distribution of the unobserved outcomes, given the observed ones, or at least implicitly imply such. One consequence is that there routinely exist a whole class of models, coinciding in their description of the observed portion of the data but differing with respect to their predictions of what is unobserved. Within such a class, there always is a single model corresponding to socalled random missingness, in the sense that the mechanism governing missingness depends on covariates and observed outcomes, but given these not further on unobserved outcomes. We employ these results in the context of socalled sharedparameter models where outcome and missingness models are connected by means of common latent variables or random effects, to devise a sensitivity analysis framework. Precisely, the impact of varying unverifiable assumptions about unobserved measurements on parameters of interest is studied. Apart from analytic considerations, the proposed methodology is applied to assess treatment effect in data from a clinical trial in toenail dermatophyte onychomycosis. While our focus is on longitudinal outcomes with incomplete outcome data, the ideas developed in this paper are of use whenever a sharedparameter model could be considered.   
Language 



English
 
Source (journal) 



Biometrische Zeitschrift = Biometrical journal  
Publication 



2010
 
Volume/pages 



52:1(2010), p. 111125
 
ISI 



000275249300009
 
Full text (Publishers DOI) 


  
