Should Traditional Intention To Treat Analysis Be Abandoned?

A commenter on my video about intention to treat analysis  asked about my thoughts on a twist on intention to treat analysis in which an adjustment is made (via an instrumental variable) for “treatment contamination”. A disclaimer: I am not a statistician or epidemiologist.

First lets start with some definitions:
1) intention to treat analysis: once randomized always analyzed in the group to which the patient was assigned (even if you don’t get the intervention in the intervention arm or you do get it in the control arm)
2) Superiority trial: study designed to “prove” one intervention is better than the other. Null hypothesis is that there is no difference between the groups.
3) Noninferiority trial: study designed to “prove” that one intervention is not worse than another treatment by some prespecified amount. Null hypothesis is the is a difference between the groups.
4) Instrumental variable: variable associated with the factor under study but not directly associated with the outcome variable or any potential confounders.

intention to treat analysis

The authors of this paper An IV for the RCT: using instrumental variables to adjust for treatment contamination in randomised controlled trials  state:

Intention to treat analysis estimates the effect of recommending a treatment to study participants, not the effect of the treatment on those study participants who actually received it. In this article, we describe a simple yet rarely used analytical technique, the “contamination adjusted intention to treat analysis,” which complements the intention to treat approach by producing a better estimate of the benefits and harms of receiving a treatment. This method uses the statistical technique of instrumental variable analysis to address contamination

So what do I think about this?
1) A main role of intention to treat (ITT) analysis is to be conservative in a superiority trial. That means we dont want to reject the null hypothesis falsely and claim treatment is better than the control. Another main role of ITT analysis is to preserve randomization (remember, once randomized always analyzed).

2) The authors of the BMJ paper point out that “Intention to treat analysis estimates the effect of recommending a treatment to study participants, not the effect of the treatment on those study participants who actually received it.” This is true but isnt that what real life is like? I recommend a treatment to my patients. Some take it, some don’t. Some who I tell not to use something wind up using it.

3) The authors of the BMJ paper further point out that ITT analysis “underestimates value of receiving the treatment.” That is possible also but its also the point (see #1 above).

4) The instrumental variable in this scheme would be a variable entered into the model indicating whether or not a patient received treatment or not (no matter what group they were assigned to). ITT analysis would still be used but be adjusted for treatment receipt. I worry that this could lead to overfitting the model- a situation where you can add too many variables to a model and start to detect noise beyond real relationships.

5) I think it would be difficult in a trial to judge adherence- what is the cutoff? Is it 100%? What about 60%? 40%? How much use by the control group is important? I think there are issues in judging what is contamination or not.

Time will tell if this technique should be used. We will have to study the treatment estimates from traditional ITT analysis and contamination adjusted ITT analysis. Until then I will stick with what is recommended…traditional ITT analysis.