Multiple imputation approaches for the analysis of dichotomized responses in longitudinal studies with missing data.

ABSTRACT: Often a binary variable is generated by dichotomizing an underlying continuous variable measured at a specific time point according to a prespecified threshold value. In the event that the underlying continuous measurements are from a longitudinal study, one can use the repeated-measures model to impute missing data on responder status as a result of subject dropout and apply the logistic regression model on the observed or otherwise imputed responder status. Standard Bayesian multiple imputation techniques (Rubin, 1987, in Multiple Imputation for Nonresponse in Surveys) that draw the parameters for the imputation model from the posterior distribution and construct the variance of parameter estimates for the analysis model as a combination of within- and between-imputation variances are fo

SUBMITTER: Lu K 

PROVIDER: S-EPMC3245577 | biostudies-literature | 2010 Dec

REPOSITORIES: biostudies-literature