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Objectives: In randomized trials, the primary analysis should be consistent with the intention-to-treat (ITT) principle and should address missing data appropriately to draw valid inferences. This review focuses on current practices relating to the ITT principle and methods to handle missing data in the major musculoskeletal journals., Study Design and Setting: A systematic review of randomized trials published in 2010 and 2011 in five musculoskeletal journals was performed., Results: We reviewed 91 trials: 38% performed a full ITT analysis (analyzing outcome data for all randomized participants) and 31% performed a partial ITT analysis (excluding participants with no follow-up data). The overall median dropout was 12%; 60% of trials had more than 10% dropouts, and 32% of trials had more than 20% dropouts. Among those that performed an ITT analysis, the majority adopted a form of single imputation; last observation carried forward was the designated approach in most cases. Mixed models for repeated measures and/or multiple imputations were limited to eight trials., Conclusion: It appears that many trials reporting missing data are inappropriately analyzed and may therefore be prone to biased estimates and invalid inferences., (Copyright © 2015 The Authors. Published by Elsevier Inc. All rights reserved.)

Introduction: When participants drop out of randomised clinical trials, as frequently happens, the intention-to-treat (ITT) principle does not apply, potentially leading to attrition bias. Data lost from patient dropout/lack of follow-up are statistically addressed by imputing, a procedure prone to bias. Deviations from the original definition of ITT are referred to as modified intention-to-treat (mITT). As yet, the impact of the potential bias associated with mITT has not been assessed. Our objective is to investigate potential bias and disadvantages of performing mITT and evaluate possible concerns when executing different mITT approaches in meta-analyses., Methods and Analysis: Using meta-epidemiology on randomised trials considered less prone to bias (ie, good internal validity) and assessing biological or targeted agents in patients with rheumatoid arthritis, we will meta-analyse data from 10 biological and targeted drugs based on collections of trials that would correspond to 10 individual meta-analyses., Ethics and Dissemination: This study will enhance transparency for evaluating mITT treatment effects described in meta-analyses. The intended audience will include healthcare researchers, policymakers and clinicians. Results of the study will be disseminated by peer-review publication., Protocol Registration: In PROSPERO CRD42013006702, 11. December 2013., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Background: Intention-to-treat (ITT) analysis requires all randomised individuals to be included in the analysis in the groups to which they were randomised. However, there is confusion about how ITT analysis should be performed in the presence of missing outcome data., Purposes: To explain, justify, and illustrate an ITT analysis strategy for randomised trials with incomplete outcome data., Methods: We consider several methods of analysis and compare their underlying assumptions, plausibility, and numbers of individuals included. We illustrate the ITT analysis strategy using data from the UK700 trial in the management of severe mental illness., Results: Depending on the assumptions made about the missing data, some methods of analysis that include all randomised individuals may be less valid than methods that do not include all randomised individuals. Furthermore, some methods of analysis that include all randomised individuals are essentially equivalent to methods that do not include all randomised individuals., Limitations: This work assumes that the aim of analysis is to obtain an accurate estimate of the difference in outcome between randomised groups and not to obtain a conservative estimate with bias against the experimental intervention., Conclusions: Clinical trials should employ an ITT analysis strategy, comprising a design that attempts to follow up all randomised individuals, a main analysis that is valid under a stated plausible assumption about the missing data, and sensitivity analyses that include all randomised individuals in order to explore the impact of departures from the assumption underlying the main analysis. Following this strategy recognises the extra uncertainty arising from missing outcomes and increases the incentive for researchers to minimise the extent of missing data.