1995
DOI: 10.2307/2533322
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An Approximate Generalized Linear Model with Random Effects for Informative Missing Data

Abstract: This paper develops a class of models to deal with missing data from longitudinal studies. We assume that separate models for the primary response and missingness (e.g., number of missed visits) are linked by a common random parameter. Such models have been developed in the econometrics (Heckman, 1979, Econometrica 47, 153-161) and biostatistics (Wu and Carroll, 1988, Biometrics 44, 175-188) literature for a Gaussian primary response. We allow the primary response, conditional on the random parameter, to follo… Show more

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Cited by 253 publications
(281 citation statements)
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“…In this case random effects are are used to affect the dependence between the responses Y and the missing mechanism M . These models are also known as shared parameter models [24,25]. In this cases the factorization of the joint model is…”
Section: Implications Of Violating the Mar Assumption Although The DImentioning
confidence: 99%
“…In this case random effects are are used to affect the dependence between the responses Y and the missing mechanism M . These models are also known as shared parameter models [24,25]. In this cases the factorization of the joint model is…”
Section: Implications Of Violating the Mar Assumption Although The DImentioning
confidence: 99%
“…This type of models have been developed as a shared parameter model (Follmann and Wu, 1995) or a shared random effects model (Gao, 2004).…”
Section: Basic Setupmentioning
confidence: 99%
“…The methods handling nonignorable missingness require both auxiliary models to be correctly specified. Many authors attacked the nonignorability problem using likelihood approach (Follmann and Wu, 1995;Ibrahim et al, 2001;Gao, 2004;Zhang and Paik, 2009), imputation approach (Paik, 1997;Yang et al, 2013), and inverse probability weighting approach Shao and Wang, 2016). Nonignorability often causes nonidentifiability which should be carefully addressed in developing methods (Wang et al, 2014;Molenberghs et al, 2008).…”
Section: Introductionmentioning
confidence: 99%
“…Therefore, nonignorable dropout mechanism is introduced. Different selection models were proposed to handle nonignorable dropouts by (Diggle and Kenward, 1994;Heckman, 1976;Little, 1995;Follmann and Wu, 1995;Crouchley and Ganjali, 2002;Yang and Li, 2011). In addition this issue was addressed in detail in advanced statistical books (Molenberghs and Kenward, 2007;Daniels and Hogan, 2008;Fitzmaurice et al, 2008;Enders, 2010).…”
Section: Jmssmentioning
confidence: 99%
“…Second, this model is sensitive to misspecification. Crouchley and Ganjali (2002) introduced a Generalized Heckman selection model and showed that the models proposed by (Wu and Carroll, 1988;Follmann and Wu, 1995;Diggle and Kenward, 1994;Ridder, 1990) can be written by this model.…”
Section: Introductionmentioning
confidence: 99%