title: A review of the handling of missing longitudinal outcome data in clinical trials.
abstract: The aim of this review was to establish the frequency with which trials take into account missingness, and to discover what methods trialists use for adjustment in randomised controlled trials with longitudinal measurements. Failing to address the problems that can arise from missing outcome data can result in misleading conclusions. Missing data should be addressed as a means of a sensitivity analysis of the complete case analysis results. One hundred publications of randomised controlled trials with longitudinal measurements were selected randomly from trial publications from the years 2005 to 2012. Information was extracted from these trials, including whether reasons for dropout were reported, what methods were used for handing the missing data, whether there was any explanation of the methods for missing data handling, and whether a statistician was involved in the analysis. The main focus of the review was on missing data post dropout rather than missing interim data. Of all the papers in the study, 9 (9%) had no missing data. More than half of the papers included in the study failed to make any attempt to explain the reasons for their choice of missing data handling method. Of the papers with clear missing data handling methods, 44 papers (50%) used adequate methods of missing data handling, whereas 30 (34%) of the papers used missing data methods which may not have been appropriate. In the remaining 17 papers (19%), it was difficult to assess the validity of the methods used. An imputation method was used in 18 papers (20%). Multiple imputation methods were introduced in 1987 and are an efficient way of accounting for missing data in general, and yet only 4 papers used these methods. Out of the 18 papers which used imputation, only 7 displayed the results as a sensitivity analysis of the complete case analysis results. 61% of the papers that used an imputation explained the reasons for their chosen method. Just under a third of the papers made no reference to reasons for missing outcome data. There was little consistency in reporting of missing data within longitudinal trials.
authors.affiliation: Institute of Translational Medicine, University of Liverpool, Crown Street, L69 3GS Liverpool, UK. M.Powney@liv.ac.uk.
authors.forename: Matthew
authors.initials: M
authors.lastname: Powney
authors.forename: Paula
authors.initials: P
authors.lastname: Williamson
authors.forename: Jamie
authors.initials: J
authors.lastname: Kirkham
authors.forename: Ruwanthi
authors.initials: R
authors.lastname: Kolamunnage-Dona
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D010352:Patient Dropouts / Q000706:statistics & numerical data*; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012107:Research Design / Q000706:statistics & numerical data*; D013997:Time Factors; D016896:Treatment Outcome