Understanding Intent To Treat Models in Medical Research

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What is the definition of Intent To Treat?

When used in medical research studies, the phrase intent to treat refers to a type of study design. In this type of study, scientists analyze the results of their study based on what the patients were told to do. In other words, doctors look at patient results  based on how they were supposed to be treated, rather than what actually happened. For example, if a person in a study is randomized to a medical treatment but ends up getting surgery -- or no treatment at all -- their outcomes are still considered as part of the medical treatment group.

In an ideal world, of course, intent to treat and actual treatment would be the same. In the real world it varies a lot, depending on the nature of what is being studied. 

Intent to treat models are used for a number of reasons. The biggest one is that, from a practical standpoint, they simply make sense. Scientists want to know how drugs or treatments will work in the real world. In the real world, not everyone takes drugs as prescribed. Not everyone ends up getting the surgery they are recommended. By using an intent to treat model, scientists can analyze how a treatment works in a slightly more realistic context. Intent to treat explicitly acknowledges the fact that how drugs work in the lab may have very little to do with how they work in the field.

Not all people like intent to treat trials. One reason is that they can underestimate a medication's potential effectiveness. For example, early trials of pre-exposure prophylaxis for HIV in gay men showed that the treatment seemed relatively effective...

but only in individuals who took it regularly. The overall results shown by the intent to treat models were a lot less encouraging. Some people say that a drug doesn't work if patients won't take it. Others say that you can't judge a medication if patients aren't taking it as prescribed.

Sometimes scientists who initially design a study for intent-to-treat analysis will end up analyzing the treatment both that way and per-protocol.

(For that they compare people who actually received the treatment as specified to those who did not, regardless of randomization.) This is usually done when the intent to treat analysis shows no effect, or no significant effect, but some effect is seen for the people who actually took the treatment. However, this type of selective, post-hoc analysis is frowned on by statisticians. It may provide misleading results for several reason. One such reason is that those who got the treatment might  be different than those who didn't.

When an intent to treat study is less promising than earlier, more closely observed studies, scientists will often ask why. This may be an attempt to salvage what had been considered to be a promising treatment. If it turns out, for example, that people weren't taking a medication because it tastes bad, that problem might be easily fixable. However, sometimes results in smaller trials simply can't be duplicated in a larger study, and doctors are never entirely sure of the reason.

Still, that's the very reason intent to treat models are important. They seek to close the understanding gap between how drugs work in research studies and how they work in the real world. That gap is a big one. 


Keene ON. Intent-to-treat analysis in the presence of off-treatment or missing data. Pharm Stat. 2011 May-Jun;10(3):191-5. doi: 10.1002/pst.421.

Matsuyama Y. A comparison of the results of intent-to-treat, per-protocol, and g-estimation in the presence of non-random treatment changes in a time-to-event non-inferiority trial. Stat Med. 2010 Sep 10;29(20):2107-16. doi: 10.1002/sim.3987

Polit DF, Gillespie BM. Intention-to-treat in randomized controlled trials: recommendations for a total trial strategy. Res Nurs Health. 2010 Aug;33(4):355-68. doi: 10.1002/nur.20386.

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