Determining the sample of participants to be analysed is a crucial step in reporting clinical trials. For such analyses, the gold standard is the “intention-to-treat” principle. The question of which participants are included in the analysis appears as Item 16 of the CONSORT statement (Box 1).1

Analysis by ITT is a strategy that compares the study groups in terms of the treatment to which they were randomly allocated, irrespective of the treatment they actually received or other trial outcomes. Regardless of protocol deviations and participant compliance or withdrawal, analysis is performed according to the assigned treatment group.2,3

Random allocation aims to ensure that trial participants’ risk factors that may affect the outcome under investigation are balanced between the allocated treatments. This is to ensure that any differences in outcomes observed between groups are actually a result of the trial interventions. Importantly, there can be no guarantee that participants from each group who do not comply with the allocated treatment have the same risk-factor profile. Any analysis other than an ITT analysis (eg, one that excludes non-compliant participants) will potentially compromise the balance of these factors and introduce bias into the treatment comparisons.

Thus, the ITT strategy generally gives a conservative estimate of the treatment effect compared with what would be expected if there was full compliance. By accepting that non-compliance and protocol deviations are likely to occur in actual clinical practice,3,4 ITT essentially tests a treatment policy or strategy, and avoids overoptimistic estimates of the efficacy of an intervention resulting from the removal of non-compliers.

A number of strategies can be adopted if the assumptions underpinning ITT are not satisfied.

If the crossover/non-compliance rates are small, then an ITT analysis should be the principal method of analysis. There is still some debate about whether ineligible subjects can legitimately be omitted from the final analysis.2 For instance, in a study involving a potentially life-threatening condition, such as severe acute respiratory syndrome, treatment may be routinely commenced before laboratory confirmation of the diagnosis. If the patients subsequently are not diagnosed with the condition, there may be a case for excluding them from the ITT population. In these instances, a “modified” or “quasi” ITT population may be defined, allowing for such exclusions. The following principles should be followed to allow participants to be excluded from such an analysis:

• the criteria for exclusion from the analysis should be pre-specified in the protocol, be objective and clearly defined;7,8 and,

• to remain unbiased, decisions to exclude participants need to be made (i) by researchers blinded to treatment allocation, and (ii) on the basis of information not related to either the allocated treatment or to events or outcomes that occur after random allocation.

In all circumstances, all patients randomly allocated to a study arm should be followed up, as exposure to study treatment may still influence their safety and place them at risk of serious adverse events. All efforts must be made to ensure maximum compliance and that patients continue to take their allocated treatments, and that all patients are accounted for in the trial report.9

The modified or quasi ITT population may also be useful when outcomes are not assessed in all participants. For example, outcomes requiring colonoscopic follow-up can result in no information for patients who, for any reason, did not undergo colonoscopy during the study, requiring an analysis based on a subset of the patient population.10 In such a case, modifying the ITT population allows some clinical interpretation of the results.

A more extreme example is a study evaluating hip protectors, in which only around 50% of those in the intervention arm were wearing a hip protector at the time of their fracture.11 In this situation, neither an ITT or per-protocol analysis would necessarily provide reliable information about the value of hip protectors when actually worn.

There has been debate about the appropriateness of imputing missing values.4 If missing data are imputed, it is recommended that some sensitivity analysis be performed to ensure that study conclusions are not misleading.4,12

ITT analysis gives unbiased and consistent estimates of a treatment policy, and should, wherever possible, be the analysis of choice. Deviations from this principle compromise the balance between groups that is achieved by random allocation, and are rarely justifiable as a principal analysis.