Inferences about drug safety in phase 3 trials in oncology: Examples from advanced prostate cancer
Menée à partir des données de 7 essais cliniques de phase III incluant au total 9 215 patients atteints d'un cancer de la prostate de stade avancé traité par apalutamide, enzalutamide ou darolutamide, cette étude identifie les différents types d'événements secondaires reportés, analyse l'homogénéité des données collectées et examine la qualité des analyses statistiques effectuées
Background: Safety is a central consideration when choosing between multiple medications with similar efficacy. We aimed to evaluate whether adverse event (AE) profiles of three such drugs in advanced prostate cancer could be distinguished based on published literature.
Methods: We assessed consistency in AE reporting, AE risk in placebo arms, and methodology used for risk estimates and quantification of statistical uncertainty in randomized placebo-controlled phase 3 trials of apalutamide, enzalutamide and darolutamide in advanced prostate cancer.
Results: Seven included clinical trials enrolled a total of 9215 participants (range = 1051 to 1715 per trial) across three prostate cancer disease states. Within disease states, baseline patient characteristics appeared similar between trials. Of 54 distinct AE types in total, only 3 (fatigue, hypertension, and seizure) were reported by all 7 trials. Absolute risks of AEs in the placebo arms differed systematically and more than two-fold between trials, which was associated with visit frequency and resulted in different degrees of uncertainty in AE profiles between trials. No trial used inferential methodology to quantify statistical uncertainty in AE risks, but 6 of 7 trials drew overall conclusions. Two trials concluded that there was no elevated AE risk due to the intervention, including the trial of darolutamide, which had the greatest statistical uncertainty.
Conclusion: Rigorous comparison of drug safety was precluded by heterogeneity in AE reporting, variation in AE risks in the placebo arms, and lack of inferential statistical methodology, underscoring considerable opportunities to improve how AE data are collected, analyzed, and interpreted in oncology trials.
Journal of the National Cancer Institute , résumé, 2019