However, if some participants are lost to follow-up baseline differences between the compared groups in the analysis may compromise the validity of trial results. To maintain this baseline comparability of the compared groups, randomised trials are routinely analysed according to the intention-to-treat principle. Hence, in a sufficiently large randomised clinical trial the compared treatment groups are expected to be comparable concerning all observed and unobserved prognostic characteristics at baseline. The key strength of randomised clinical trials is that random allocation of participants results in similar baseline characteristics in the compared groups – if enough participants are randomised. We present a practical guide and flowcharts describing when and how multiple imputation should be used to handle missing data in randomised clinical. We also present practical flowcharts on how to deal with missing data and an overview of the steps that always need to be considered during the analysis stage of a trial. We consider the strengths and limitations of using of best-worst and worst-best sensitivity analyses, multiple imputation, and full information maximum likelihood. We consider how to optimise the handling of missing data during the planning stage of a randomised clinical trial and recommend analytical approaches which may prevent bias caused by unavoidable missing data. Handling missing data is an important, yet difficult and complex task when analysing results of randomised clinical trials. We also searched PubMed (key words: missing data randomi* statistical analysis) and reference lists of known studies for papers (theoretical papers empirical studies simulation studies etc.) on how to deal with missing data when analysing randomised clinical trials. The authors had several meetings and discussions considering optimal ways of handling missing data to minimise the bias potential. Therefore, the analysis of trial data with missing values requires careful planning and attention. The potential bias due to missing data depends on the mechanism causing the data to be missing, and the analytical methods applied to amend the missingness. Missing data may seriously compromise inferences from randomised clinical trials, especially if missing data are not handled appropriately.
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