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16.2.3  Intention-to-treat issues for continuous data

In full ITT analyses, all participants who did not receive the assigned intervention according to the protocol as well as those who were lost to follow-up are included in the analysis. Inclusion of these in an analysis requires that means and standard deviations of the outcome for all randomized participants are available. As for dichotomous data, dropout rates should always be collected and reported in a ‘Risk of bias’ table. Again, there are two basic options, and in either case a sensitivity analysis should be performed (see Chapter 9, Section 9.7).


A simple way to conduct a sensitivity analysis for continuous data is to assume a fixed difference between the actual mean for the missing data and the mean assumed by the analysis. For example, after an analysis of available cases, one could consider how the results would have differed if the missing data in the intervention arm had averaged 2 units greater than the observed data in the intervention arm, and the missing data in the control arm had averaged 2 units less than the observed data in the control arm. A Bayesian approach, which automatically down-weights studies with more missing data, has been considered (White 2007).