Multiple-Imputation Variance Estimation in Studies With Missing or Misclassified Inclusion Criteria.

ABSTRACT: In observational studies using routinely collected data, a variable with a high level of missingness or misclassification may determine whether an observation is included in the analysis. In settings where inclusion criteria are assessed after imputation, the popular multiple-imputation variance estimator proposed by Rubin ("Rubin's rules" (RR)) is biased due to incompatibility between imputation and analysis models. While alternative approaches exist, most analysts are not familiar with them. Using partially validated data from a human immunodeficiency virus cohort, we illustrate the calculation of an imputation variance estimator proposed by Robins and Wang (RW) in a scenario where the study exclusion criteria are based on a variable that must be imputed. In this motivating example, the

SUBMITTER: Giganti MJ 

PROVIDER: S-EPMC7705600 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature