In clinical trials it is most typical that some participant outcomes will not be available. This may be due to missed participant visits, trial withdrawal or other unplanned and uncontrollable events. Therefore often some required data will be missing from the analysis. When there are missing data, it is important that the primary analysis of the trial is conducted under the most plausible assumption for the missing data. Sensitivity analysis under a range of different credible assumptions should then be undertaken to assess how robust the trial results are. One method which readily enables contextually relevant sensitivity analysis, and has recently seen increased discussion and developments in the statistical literature, is Controlled Multiple Imputation. In this new tutorial article, an overview of Controlled Multiple Imputation procedures, and a practical guide to their use for sensitivity analysis of a continuous outcome is provided. Worked examples and Stata code are included to facilitate adoption of such methods, to enable robust evaluation of clinical trial results.