In a clinical trial (we only talk about superiority trials here as the situation is different for non-inferiority trials), one wants to detect a benefit of treatment A (e.g. verum) compared to treatment B (e.g. placebo). The aim is to disprove that “treatment A is not better than treatment B (so-called “null hypothesis”). This is equivalent to a proof that “treatment A is actually better than treatment B” (that is the way statistical tests work).
Thus, a high treatment effect leads to a successful trial (i.e. to proven efficacy). However, if you choose a too optimistic method of analysis, i.e. if you over-estimate the effect, you receive more likely a positive result. Or in other words: you increase the probability of a type I error.
Therefore, in clinical trials any over-estimation of the effect needs to be avoided. With respect to prevention of type I error it is still better to choose a method which under-estimates the effect (conservative approach) than a method which might over-estimate it.
What does this general rule mean for the choice of ITT vs. PP? What is the more conservative approach in this context? The simple answer is: it’s the analysis according to the ITT principle.
For this kind of analysis, actual treatment effects usually are watered-down, or in other words: effects are under-estimated. This tendency is also described in common guidelines (e.g. ICH E9). It can be derived from the fact that in the full analysis set also non-compliant patients are included and non-compliance generally is associated with a negative outcome (e.g., patients who dropped out at a very early stage in the study usually have a negative outcome). Presumed that non-compliance occurs in all treatment arms, differences between the treatments consequently diminish.