Reporting missing participant data in randomised trials: systematic survey of the methodological literature and a proposed guide
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ObjectivesWe conducted a systematic survey of the methodological literature to identify recommended approaches for how and what randomised clinical trial (RCT) authors should report on missing participant data and, on the basis of these approaches, to propose guidance for RCT authors.MethodsWe defined missing participant data (MPD) as missing outcome data for trial participants. We considered both categorical and continuous outcome data. We searched MEDLINE and the Cochrane Methodology Register for articles in which authors proposed approaches to reporting MPD from RCTs. We selected eligible articles independently and in duplicate and extracted data in duplicate. Using an iterative process of discussion and revisions, we used the findings to develop guidance.ResultsOf 10 501 unique citations identified, 13 articles reporting on 10 approaches proved eligible. The identified approaches recommend reporting the following aspects (from most to least frequently recommended): number of participants with MPD (n=10), reasons for MPD (n=7), methods used to handle MPD in the analysis (n=4), flow of participants (n=3), pattern of missingness (eg, whether at random) (n=3), differences in rates of MPD between trial arms (n=2), differences between participants with and without MPD (n=2), results of any sensitivity analyses (n=2), implication of MPD on interpreting the results (n=2) and methods used to prevent missing data (n=1). We propose a guide with nine items related to reporting the number, reasons, patterns, analytical methods and interpretation of MPD.ConclusionsMost identified approaches invite trial authors to report the extent of MPD and the underlying reasons. Fewer approaches focus on reporting missingness patterns, methods for handling MPD and implications of MPD on results. Our proposed guidance could help RCT authors to better report, and readers to better identify participants with missing data.
