Moment reconstruction and moment-adjusted imputation when exposure is generated by a complex, nonlinear random effects modeling process.

ABSTRACT: For the classical, homoscedastic measurement error model, moment reconstruction (Freedman et al., 2004, 2008) and moment-adjusted imputation (Thomas et al., 2011) are appealing, computationally simple imputation-like methods for general model fitting. Like classical regression calibration, the idea is to replace the unobserved variable subject to measurement error with a proxy that can be used in a variety of analyses. Moment reconstruction and moment-adjusted imputation differ from regression calibration in that they attempt to match multiple features of the latent variable, and also to match some of the latent variable's relationships with the response and additional covariates. In this note, we consider a problem where true exposure is generated by a complex, nonlinear random effects mo

SUBMITTER: Potgieter CJ 

PROVIDER: S-EPMC5055848 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature