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

ABSTRACT:

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 coronary intervention followed with use of repetitive questionnaires and information from national registers over 3 years, only 417 out of 1,726 patients had complete data on all measure points and covariates. We suggest strategies for use of MI and different methods for dealing with death along with sensitivity analysis of deviations from the assumption of missing at random, all with the

SUBMITTER: Biering K 

PROVIDER: S-EPMC4303367 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature