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The ability to leave comments expires after a certain period of time. Also, linked history inside goes back 5 years just on Grail apparently trying to become Holy Grail. Comments 1 Please note , comments are no longer published through this website. Dhritiman Chakrabarti June 5, at am Seems like choosing lesser of the two evils. Reply on Twitter Retweet on Twitter 6 Like on Twitter 2 Twitter Retweet on Twitter HealthNewsReview.
Reply on Twitter Retweet on Twitter 3 Like on Twitter 5 Twitter Reply on Twitter Retweet on Twitter 3 Like on Twitter 2 Twitter 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.
Thus, a high treatment effect leads to a successful trial i. However, if you choose a too optimistic method of analysis, i. 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. What is the more conservative approach in this context? 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. 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. Presumed that non-compliance occurs in all treatment arms, differences between the treatments consequently diminish.
Consider a superiority trial with two treatment arms verum vs. The real response rates, i. Due to their shortened observation period, none of the dropouts achieved response a reasonable assumption.
Nevertheless, according to the ITT principle, all patients including dropouts are included in the full analysis set. How about the PP analysis in this context? Exclusion of patients from the analysis due to major protocol deviations can of course also cause a tendency to wrong estimations of a treatment effect. This is particularly the case, if the frequency of and the reasons for exclusion vary between the study groups. However, for a PP analysis it is not straightforward to pre-guess the direction of a wrong estimation over- or under-estimation.
Some authors and guidelines claim a tendency of PP analyses to over-estimate an effect e. ICH E9 guideline although this cannot be derived mathematically. Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
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