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Medicine

An intention-to-treat analysis preserves the assigned groups

Archive 01206

Slide 1

Intention-to-treat keeps people in their assigned groups

An intention-to-treat analysis compares participants in the groups to which they were randomized. It keeps that assignment even when treatment use changes later.

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This estimates the effect of assignment to a strategy under the trial’s conditions. It is a different question from asking what would happen if every participant followed the treatment exactly as planned.

Slide 2

Excluding people who change treatment can bias the comparison

People who stop or change treatment may differ systematically from those who continue. Excluding them can distort the randomized comparison.

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A departure might relate to side effects, worsening illness or practical obstacles. Those reasons can also relate to outcomes, so removing departures is not necessarily a neutral cleanup of the data.

Slide 3

The label alone does not show who was analyzed

Reports use intention-to-treat terminology inconsistently. Readers need to inspect who was actually included and how the groups were analyzed.

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A survey of published randomized trials found variation in how the term was applied. A methods label is therefore weaker evidence than a transparent account of exclusions and analysis decisions.

Slide 4

Keeping an assignment cannot recover a missing outcome

Keeping a person’s assigned group does not reveal an outcome that was never measured. Missing data require explicit methods and assumptions.

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Analyses can explore how conclusions change under different plausible assumptions about missing outcomes. Such sensitivity checks help expose dependence on information that the study could not directly observe.

Slide 5

Assignment and actual treatment use answer different questions

Assignment and adherence effects can both be useful. They should not be confused, because they describe different consequences of an intervention strategy.

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A simple comparison restricted to adherent participants does not automatically recover a causal adherence effect. Departures after randomization create an analysis problem that requires more than deleting inconvenient records.

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