Why These Models Are Used
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. In fact, one of the reasons that promising drugs are often so disappointing when they’re released is that people don’t take them the way they do in the studies. (There are also often other differences between real-world patients and research patients.)
Drawbacks
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. Both sides have a point. There is no perfect answer. Which analysis makes the most sense to use is somewhat dependent on the question.
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 a per-protocol analysis, 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 reasons. 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.
The truth is, the differences seen between early efficacy trials and intent to treat studies, are the very reason intent to treat models are important. This type of study seeks to close the understanding gap between how drugs work in research studies and how they work in the real world. That gap can be a big one.
