A review of the reporting and handling of missing data in cohort studies with repeated assessment of exposure measures

A systematic search of English language papers published from January 2000 to December 2009 was carried out in PubMed. Prospective cohort studies with a sample size greater than 1,000 that analysed data using repeated measures of exposure were included.Among the 82 papers meeting the inclusion criteria, only 35 (43%) reported the amount of missing data according to the suggested guidelines. Sixty-eight papers (83%) described how they dealt with missing data in the analysis. Most of the papers excluded participants with missing data and performed a complete-case analysis (n？=？54, 66%). Other papers used more sophisticated methods including multiple imputation (n？=？5) or fully Bayesian modeling (n？=？1). Methods known to produce biased results were also used, for example, Last Observation Carried Forward (n？=？7), the missing indicator method (n？=？1), and mean value substitution (n？=？3). For the remaining 14 papers, the method used to handle missing data in the analysis was not stated.This review highlights the inconsistent reporting of missing data in cohort studies and the continuing use of inappropriate methods to handle missing data in the analysis. Epidemiological journals should invoke the STROBE guidelines as a framework for authors so that the amount of missing data and how this was accounted for in the analysis is transparent in the reporting of cohort studies.