intentiontotreatanalys

Intention-to-treat analysis is a principle used in the analysis of randomized controlled trials in which all participants who were randomized are included in the analysis and analyzed in the groups to which they were assigned, regardless of adherence to the allocated intervention, withdrawal, noncompliance, or protocol deviations. The aim is to preserve the benefits of randomization and provide an estimate of treatment effect that reflects real-world use.

ITTa analyses help prevent bias that can arise from dropping or reassigning participants based on post-randomization events. They support the generalizability of trial results and maintain the balance between groups. In practice, handling missing data and incomplete follow-up is a critical challenge; researchers employ methods such as multiple imputation, mixed-effects models, or sensitivity analyses. Historically, approaches like last observation carried forward have been discouraged in favor of more robust methods.

Limitations of ITT include potential dilution of the treatment effect when nonadherence is high or when there is substantial missing outcome data. While it provides an estimate of effectiveness in typical use, it may not reflect efficacy under perfect adherence. Therefore ITT is often complemented by additional analyses, such as per-protocol analyses that include only participants who completed the study according to the protocol, and as-treated analyses that categorize participants by the treatment they actually received.

In noninferiority trials, ITT analyses may bias toward showing than noninferiority, so guidelines commonly recommend reporting both ITT and per-protocol analyses. Variants such as modified intention-to-treat define the population more narrowly, depending on trial design and eligibility criteria.