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Comparisons of treatments applied to people, animals, etc.  

(Intent to Treat; Comparisons with Drop-Outs)

In many forms of comparison of two treatments involving human subjects, there are subjects who do not complete the treatment. They may die, move away, encounter life circumstances which take priority, or just decide for whatever reason to drop out of the study or not do all that they are asked. It is tempting to just analyze the data for those completing the protocol, essentially ignoring the drop-outs. This is usually a serious mistake, for two reasons:

1. In a good study, subjects should be randomized to treatment. Just analyzing data for those who complete the protocol destroys the randomization, so that model assumptions are not satisfied. To preserve the randomization, outcomes for all subjects assigned to each group need to be compared. This is called intent-to-treat (or intention-to-treat, or ITT) analysis.

2. Intent-to-treat analysis is usually more informative for consumers of the research. For example, in studying two drug treatments, dropouts for reasons not related to the treatment can be expected to be, on average, roughly the same for both groups. But if one drug has serious side-effects that prompt patients to discontinue use, that would show up in the drop-out rate, and be important information in deciding which drug to use or recommend.

Reason 1 (and sometimes also reason 2) also applies when treatments are applied to animals, plants, or even objects.

For more information on intent-to-treat analysis, see the following:

Freedman, DA (2005)  Statistical Models: Theory and Practice, pp. 5, 15

D.A. Freedman. “Statistical models for causation: What inferential leverage do they provide?” Evaluation Review vol. 30 (2006) pp. 691–713. Preprint

van Belle (2008) Statistical Rules of Thumb, p. 156 - 157