Contrary to popular belief, ITT analysis was more conservative than PP analysis in the majority of antibiotic non-inferiority studies. The lower treatment success rate in the ITT analysis resulted in greater variance and a wider CI, resulting in a more conservative lower limit of CI. THE ITT analysis should be mandatory and should be considered as a primary or co-primary analysis for non-inferiority studies. any major deviation from the protocol (para. B taking a concomitant drug that affects the primary endpoint) Center for Drug Evaluation and Research (CDER). Guide to industrially acquired bacterial pneumonia: development of drugs for treatment. 2014. www.fda.gov/media/75149/download. Retrieved 8 June 2020. While an ITT analysis aims to preserve the original randomization and avoid possible biases due to the exclusion of patients, the objective of a pro-protocol (PP) analysis is to identify a therapeutic effect that would occur under optimal conditions; == References ===== External links ===* Official website Therefore, some patients (from the full set of analyses) should be excluded from the population (PP population) used for PP analysis. Of the 227 studies of non-inferiority of antibiotics, 41 (18.1%) studies included only ITT analyses, 22 (9.7%) studies reported only PP analyses and 164 (72.2%) studies reported both ITT and PP analyses. In addition, nine studies were excluded to report primary outcomes that were not proportionate. One study was excluded because it did not report the numbers needed to calculate treatment success rates.
Thus, 154 (67.8%) studies met the inclusion criteria (Additional Act 1: Table 1 of the Schedule). Of these studies, eight studies had three arms and reported two comparisons. One study had four arms and reported three comparisons. Therefore, 164 comparisons were included in the analysis (Fig. 2). Sometimes non-compliance is related to a particular intervention or the severity of the disease. For example, the inability to complete the planned treatment or the appearance of unacceptable side effects may be more common in patients with severe illness. In addition, these may be more common in the active treatment arm than in the placebo arm. Therefore, exclusion of participants who do not complete treatment or follow-up as planned would result in differential exclusion of patients with severe disease in the treated group, with the remaining group likely not resembling the initial group received at randomization. This can make treatment better than it actually is What about PP analysis in this context? Of course, excluding patients from the analysis due to large protocol deviations can also lead to a tendency to incorrect assessments of a treatment effect.
This is particularly the case when the frequency and grounds for exclusion vary from one study group to another. However, for a PP analysis, it is not easy to guess in advance the direction of an incorrect estimate (overestimation or underestimation). Some authors and guidelines argue that PP analyses tend to overestimate an effect (e.B. ICH Guideline E9), although this cannot be mathematically inferred. In interventional studies, a subset of participants often do not conform to the protocol. These “protocol violations” can be of different types: one or more participants, for whatever reason, do not receive the respective interventions for which they were randomized, inadvertently receive an intervention intended for the other branch of the study, receive a prohibited accompanying intervention or are not available for evaluation of the expected outcome, either because of the loss for follow-up, or for some other reason. [1] When analyzing the results of the study, the researcher is tempted to exclude these “non-conformist” participants. The motivation is not deception, but integrity to ensure that comparisons are made between participants in each arm of the study who strictly adhered to the intended treatment so that the true effectiveness of one intervention over the other can be assessed. Consider a superiority study with two treatment arms (verum vs placebo) with a dichotomous outcome (yes, no answer). The actual response rates, i.e.
the expected response rates, are 60% with Verum and 40% with placebo; so there is a real treatment effect of 20% points. Deviations that could be affected by actual treatment should not be used as exclusion criteria: e.B. “Early study discontinuation” may not be a good exclusion criterion from PP analysis if this discontinuation is due to lack of efficacy (and is therefore associated with the treatment received). In randomised controlled trials (RCTs), the most frequently analysed populations are intention-to-treat (ITT) and protocol (PP) populations [1, 2]. The ITT population includes all patients who are analyzed in their randomized treatment arms, whether they have used the treatment or completed the study [1]. .