2006
DOI: 10.1002/sim.2593
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Incorporating missingness for estimation of marginal regression models with multiple source predictors

Abstract: SummaryMultiple informant data refers to information obtained from different individuals or sources used to measure the same construct; for example, researchers might collect information regarding child psychopathology from the child's teacher and the child's parent. Frequently, studies with multiple informants have incomplete observations; in some cases the missingness of informants is substantial. We introduce a Maximum Likelihood (ML) technique to fit models with multiple informants as predictors that permi… Show more

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Cited by 15 publications
(18 citation statements)
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References 22 publications
(42 reference statements)
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“…The associations of log 10 -transformed prenatal and childhood PBDEs (1, 2, 3, 5, and 8 years) and visual spatial learning at 8 years were estimated using multiple informant models (Horton et al, 1999; Litman et al, 2007), which account for repeated measurements of PBDEs (prenatal and childhood) as well as the repeated measures of the Virtual Morris Water Maze (time and distance) by implementing a non-standard version of generalized estimating equations as described by Sanchez et al (2011). Each model included repeated measurements of BDE congeners or Σ 5 PBDEs in a long-formatted dataset (each observation was for one exposure window and one block performance measure [time, distance]).…”
Section: Methodsmentioning
confidence: 99%
“…The associations of log 10 -transformed prenatal and childhood PBDEs (1, 2, 3, 5, and 8 years) and visual spatial learning at 8 years were estimated using multiple informant models (Horton et al, 1999; Litman et al, 2007), which account for repeated measurements of PBDEs (prenatal and childhood) as well as the repeated measures of the Virtual Morris Water Maze (time and distance) by implementing a non-standard version of generalized estimating equations as described by Sanchez et al (2011). Each model included repeated measurements of BDE congeners or Σ 5 PBDEs in a long-formatted dataset (each observation was for one exposure window and one block performance measure [time, distance]).…”
Section: Methodsmentioning
confidence: 99%
“…We investigated associations between log 10 -transformed child serum PBDEs and FSIQ and Externalizing Problems at 8 years with multiple informant models (Horton et al, 1999; Litman et al, 2007), which are non-standard versions of generalized estimating equations that allow for repeated environmental chemical measurements (Sanchez et al, 2011). This method allows us to identify windows of susceptibility for PBDE neurotoxicity by including interaction terms between child age and PBDE concentrations.…”
Section: Methodsmentioning
confidence: 99%
“…Multiple informant models were used to estimate βs and 95% confidence intervals (CIs) between log 10 -transformed lipid-adjusted prenatal and postnatal PBDE concentrations (BDE-28, −47, −99, −100, −153, and ΣPBDEs) and continuous CPT-II measures at 8 years in each of the 100 imputed datasets (Horton et al, 1999; Litman et al, 2007). These models allow for repeated PBDE concentrations throughout childhood to be included within one model and therefore allowed us to examine PBDE neurotoxicity at different exposure time points, spanning from in utero to school-age.…”
Section: Methodsmentioning
confidence: 99%