Abstract
BackgroundMissing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. We aimed to evaluate the extent, handling, and sensitivity analysis of missing data and intention-to-treat (ITT) analysis of randomized controlled trials (RCTs) in top tier medical journals, and compare our findings with previous reviews related to missing data and ITT in RCTs.MethodsReview of RCTs published between July and December 2013 in the BMJ, JAMA, Lancet, and New England Journal of Medicine, excluding cluster randomized trials and trials whose primary outcome was survival.ResultsOf the 77 identified eligible articles, 73 (95%) reported some missing outcome data. The median percentage of participants with a missing outcome was 9% (range 0 – 70%). The most commonly used method to handle missing data in the primary analysis was complete case analysis (33, 45%), while 20 (27%) performed simple imputation, 15 (19%) used model based methods, and 6 (8%) used multiple imputation. 27 (35%) trials with missing data reported a sensitivity analysis. However, most did not alter the assumptions of missing data from the primary analysis. Reports of ITT or modified ITT were found in 52 (85%) trials, with 21 (40%) of them including all randomized participants. A comparison to a review of trials reported in 2001 showed that missing data rates and approaches are similar, but the use of the term ITT has increased, as has the report of sensitivity analysis.ConclusionsMissing outcome data continues to be a common problem in RCTs. Definitions of the ITT approach remain inconsistent across trials. A large gap is apparent between statistical methods research related to missing data and use of these methods in application settings, including RCTs in top medical journals.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2288-14-118) contains supplementary material, which is available to authorized users.
Highlights
Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials
If after taking observed data into account there are no systematic differences between participants with complete data as compared to those with missing data, data are considered to be missing at random (MAR)
We identified the statistical method used to handle missing data in the principal analysis and classified these as complete case, simple imputation, multiple imputation or model based
Summary
Missing outcome data is a threat to the validity of treatment effect estimates in randomized controlled trials. If missingness of the outcome of interest is unrelated to observed or unobserved patient data, the missing data are termed missing completely at random (MCAR): a strong assumption. If data are MCAR, analyzing only those with observed outcome data (complete case analysis) will result in some loss of efficiency but unbiased estimation [5,6,7]. If after taking observed data into account there are no systematic differences between participants with complete data as compared to those with missing data , data are considered to be missing at random (MAR). Missing outcomes are termed missing not at random (MNAR) if systematic differences between dropouts and completers persist even after taking observed data into account. Sensitivity analyses should be performed under different assumptions than the primary analysis for example, if the primary analysis makes a MCAR assumption, the sensitivity analyses should assume MAR or MNAR
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