LAUSR

In randomized controlled trials, randomization creates groups that are reasonably well balanced on all baseline variables, whether measured, unmeasured, or unknown. Postbaseline events disturb this balance, resulting in postrandomization biases. Drop-out is one such event. There are two main methods for data analysis when there are dropouts. One method is to analyze data from only those who complete the study (completer analysis), or only those who complete the study and also comply with all its key elements (per-protocol analysis, a special type of completer analysis). The other method is to analyze the data from all randomized patients, regardless of dropout (intent-to-treat [ITT] analysis), or all randomized patients who meet an additional criterion, such as taking at least one dose of study drug (modified ITT [mITT] analysis, a special type of ITT analysis). Completer analyses present results in the ideal situation in which patients take medications as advised. ITT analyses present results related to real-world practice, where patients may be irregular with dosing or stop taking medications. The advantages and disadvantages of each type of analysis are discussed. The handling of missing data in ITT and mITT analysis is also briefly discussed.