So, I'm reviewing a medical study (open-label trial) that compares the efficacy of different drug doses on a patient population (heart failure). The study is arguably of low-quality, compared to the landmark trials that established the benefit of this drug in this population: low sample size (~100 compared to 2000), short follow-up duration (~3 months compared to 16 months), open-label and without placebo.
The results the authors report are too good to be true - the study arm that can be directly compared with previous trials on this topic (same population, same drug dose) had a 4 times greater reduction in NT-proBNP (heart failure biomarker) than in the original study! And remember! Only in 3 months compared to 16!
Furthermore, during previous peer review, another reviewer suggested that it is a study limitation that no markers of functional capacity were available (6-minute walk test), and the authors just included it, seemingly out of thin air! (It wasn't previously mentioned in the methodology of the study).
Another indication that their data is falsified is that they report that all their measured variables were normally distributed - in my experience with similar variables, they are normally log-normal, not outright normal! (Although there is no proof that's always the case in literature).
The editor is seemingly hell-bent on publishing this paper, as it has undergone 4 rounds of review, and reviewers that reject it are being replaced one-by-one.
I know I can't reject this paper on the strong suspicion of foul play - what is the right way to tackle this?
EDIT: Thank you for your responses and comments. The authors ascribe the discrepancy to a different sample make-up, but it's (subjectively) too great to be simply due to the sample composition. As for anonymity, it didn't occur to me initially - I'll try to maintain enough ambiguity as to prevent a breach of blinding.