How should attrition be handled, and what analysis preserves randomization if attrition occurs?

How should attrition be handled, and what analysis preserves randomization if attrition occurs?

• A. If attrition occurs, investigators should perform an intention-to-treat analysis to preserve randomization.
• B. Attrition has no effect on study power.
• C. Intention-to-treat analysis is only used in non-randomized studies.
• D. Attrition automatically invalidates all study findings.

Answer: (A)

Preserving the benefits of randomization when participants drop out or don’t adhere to the assigned treatment is achieved by using an intention-to-treat analysis. An ITT approach analyzes participants according to the group they were randomized to, regardless of whether they actually received or completed the treatment. This preserves the original balance between groups created by randomization, preventing attrition-related bias that can occur if you only analyze those who stayed or who followed the protocol. ITT provides an estimate of the effect of being assigned to a treatment, which aligns with how decisions are made in real-world practice and keeps the comparison fair. Attrition does reduce study power, and missing data must be handled appropriately within ITT (for example, through appropriate imputation methods or models that accommodate missingness). The other statements are not correct because attrition can affect power, ITT is not limited to non-randomized studies, and attrition does not automatically invalidate findings—it can bias results if not properly addressed, which ITT helps mitigate.

Preserving the benefits of randomization when participants drop out or don’t adhere to the assigned treatment is achieved by using an intention-to-treat analysis. An ITT approach analyzes participants according to the group they were randomized to, regardless of whether they actually received or completed the treatment. This preserves the original balance between groups created by randomization, preventing attrition-related bias that can occur if you only analyze those who stayed or who followed the protocol. ITT provides an estimate of the effect of being assigned to a treatment, which aligns with how decisions are made in real-world practice and keeps the comparison fair.

Attrition does reduce study power, and missing data must be handled appropriately within ITT (for example, through appropriate imputation methods or models that accommodate missingness). The other statements are not correct because attrition can affect power, ITT is not limited to non-randomized studies, and attrition does not automatically invalidate findings—it can bias results if not properly addressed, which ITT helps mitigate.