Intent-to-treat analysis represents the gold standard primary analysis approach for randomized controlled trials because it preserves the benefits of randomization and reflects real-world treatment effectiveness. By analyzing participants according to their randomized assignment rather than their actual treatment experience, ITT maintains the baseline comparability between groups established through randomization. This approach prevents the introduction of selection bias that could occur if participants were analyzed based on post-randomization characteristics or behaviors.

The ITT principle requires that every randomized participant be included in the analysis, typically in the group to which they were assigned, regardless of protocol adherence, treatment received, or study completion. Participants who discontinue treatment, cross over to alternative therapies, or are lost to follow-up remain in their original assigned groups. This requirement creates practical challenges when outcome data are missing, necessitating appropriate methods for handling missing data that maintain the validity of statistical inference.

ITT analysis provides a conservative estimate of treatment effect that reflects how the treatment would perform in actual clinical practice, where not all patients adhere perfectly to prescribed regimens. While ITT may underestimate the efficacy achievable with perfect adherence, it provides a more realistic assessment of expected benefit when the treatment is used in routine care. Regulatory authorities generally require ITT analysis as the primary analysis for confirmatory trials, though supplementary analyses using other populations may provide additional insights.

Intent-to-treat analysis represents the gold standard primary analysis approach for randomized controlled trials because it preserves the benefits of randomization and reflects real-world treatment effectiveness. By analyzing participants according to their randomized assignment rather than their actual treatment experience, ITT maintains the baseline comparability between groups established through randomization. This approach prevents the introduction of selection bias that could occur if participants were analyzed based on post-randomization characteristics or behaviors.

The ITT principle requires that every randomized participant be included in the analysis, typically in the group to which they were assigned, regardless of protocol adherence, treatment received, or study completion. Participants who discontinue treatment, cross over to alternative therapies, or are lost to follow-up remain in their original assigned groups. This requirement creates practical challenges when outcome data are missing, necessitating appropriate methods for handling missing data that maintain the validity of statistical inference.

ITT analysis provides a conservative estimate of treatment effect that reflects how the treatment would perform in actual clinical practice, where not all patients adhere perfectly to prescribed regimens. While ITT may underestimate the efficacy achievable with perfect adherence, it provides a more realistic assessment of expected benefit when the treatment is used in routine care. Regulatory authorities generally require ITT analysis as the primary analysis for confirmatory trials, though supplementary analyses using other populations may provide additional insights.