R&D, Attrition and Multiple Imputation in BRDIS

Multiple Imputation Methodology for Missing Data, Non-Random Response, and Panel Attrition

Using multiple imputation to deal with missing data and attrition in longitudinal studies with repeated measures of patient-reported outcomes

OBJECTIVE Missing data is a ubiquitous problem in studies using patient-reported measures, decreasing sample sizes and causing possible bias. In longitudinal studies, special problems relate to attrition and death during follow-up. We describe a methodological approach for the use of multiple imputation (MI) to meet these challenges. METHODS In a cohort of patients treated with percutaneous c...

Population inference with mortality and attrition in longitudinal studies on aging: a two-stage multiple imputation method.

A major challenge for inference regarding aging-related change in longitudinal studies is that of study attrition and population mortality. Inferences in longitudinal studies can account for attrition and mortality-related change as distinct processes, but this is made difficult when follow-up of all individuals (i.e., age at death) is not complete. This is a common problem because most longitu...

Multiple imputation in quantile regression.

We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root-n consistent and asymptotically normal. To protect against possible model misspecification, we further propose a...

Multiple Imputation in Two Stages

Conventional multiple imputation (MI) (Rubin, 1987) replaces the missing values in a dataset by m > 1 sets of simulated values. We describe a two-stage extension of MI in which the missing values are partitioned into two groups and imputed N = mn times in a nested fashion. Two-stage MI divides the missing information into two components of variability, lending insight when the missing values ar...