Multiple Imputation for Incomplete Data in Epidemiologic Studies.

ABSTRACT: Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete-case analysis can reduce the efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods for mitigating the influence of missing information, such as multiple imputation, are becoming an increasing popular strategy in order to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we de

SUBMITTER: Harel O 

PROVIDER: S-EPMC5860387 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature