BACKGROUND: Collection and reporting of adverse events (AEs) and their relatedness to study treatment, known commonly as attribution, in clinical trials is mandated by regulatory agencies (the National Cancer Institute and the Food and Drug Administration). Attribution is assigned by the treating physician using judgment based on various factors including patient's baseline status, disease history, and comorbidity as well as knowledge about the safety profile of the study treatments. We evaluate the patterns of AE attribution (unrelated, unlikely, possibly, probably, and definitely related to the treatment) in treatment, symptom intervention (cancer patients) and cancer prevention (participants at high risk for cancer) setting. MATERIALS AND METHODS: Nine multicenter placebo-controlled trials (two treatment, two symptom intervention, and five cancer prevention) were analysed separately (2155 patients). Frequency and severity of AEs were summarized by arm. Attribution and percentage of repeated AEs whose attribution changed overtime were summarized for the placebo arms. Percentage of physician over- or under-reporting of AE relatedness was calculated for the treatment arms using the placebo arm as the reference. RESULTS: Across all trials and settings, a very high proportion of AEs reported as related to treatment were classified as possibly related, a significant proportion of AEs in the placebo arm were incorrectly reported as related to treatment, and clinician-reported attribution over-estimated the rate of AEs related to treatment. Fatigue, nausea, vomiting, diarrhea, constipation, and neurosensory were the common AEs that were over reported by clinician as related to treatment. CONCLUSIONS: These analyses demonstrate that assigning causality to AE is a complex and difficult process that produces unreliable and subjective data. In randomized double-blind placebo-controlled trials where data are available to objectively assess relatedness of AE to treatment, attribution assignment should be eliminated.
BACKGROUND: Collection and reporting of adverse events (AEs) and their relatedness to study treatment, known commonly as attribution, in clinical trials is mandated by regulatory agencies (the National Cancer Institute and the Food and Drug Administration). Attribution is assigned by the treating physician using judgment based on various factors including patient's baseline status, disease history, and comorbidity as well as knowledge about the safety profile of the study treatments. We evaluate the patterns of AE attribution (unrelated, unlikely, possibly, probably, and definitely related to the treatment) in treatment, symptom intervention (cancer patients) and cancer prevention (participants at high risk for cancer) setting. MATERIALS AND METHODS: Nine multicenter placebo-controlled trials (two treatment, two symptom intervention, and five cancer prevention) were analysed separately (2155 patients). Frequency and severity of AEs were summarized by arm. Attribution and percentage of repeated AEs whose attribution changed overtime were summarized for the placebo arms. Percentage of physician over- or under-reporting of AE relatedness was calculated for the treatment arms using the placebo arm as the reference. RESULTS: Across all trials and settings, a very high proportion of AEs reported as related to treatment were classified as possibly related, a significant proportion of AEs in the placebo arm were incorrectly reported as related to treatment, and clinician-reported attribution over-estimated the rate of AEs related to treatment. Fatigue, nausea, vomiting, diarrhea, constipation, and neurosensory were the common AEs that were over reported by clinician as related to treatment. CONCLUSIONS: These analyses demonstrate that assigning causality to AE is a complex and difficult process that produces unreliable and subjective data. In randomized double-blind placebo-controlled trials where data are available to objectively assess relatedness of AE to treatment, attribution assignment should be eliminated.
Authors: Jared C Foster; Jennifer G Le-Rademacher; Josephine L Feliciano; Ajeet Gajra; Drew K Seisler; Ronald DeMatteo; Jacqueline M Lafky; Arti Hurria; Hyman B Muss; Harvey J Cohen; Aminah Jatoi Journal: Cancer Date: 2017-07-12 Impact factor: 6.860
Authors: Charlene M Fares; Timothy J Williamson; Matthew K Theisen; Amy Cummings; Krikor Bornazyan; James Carroll; Marshall L Spiegel; Annette L Stanton; Edward B Garon Journal: JCO Clin Cancer Inform Date: 2018-12
Authors: Jennifer G Le-Rademacher; Elizabeth M Storrick; Aminah Jatoi; Sumithra J Mandrekar Journal: Mayo Clin Proc Innov Qual Outcomes Date: 2019-05-27
Authors: Conor D Tweed; Angela M Crook; Evans I Amukoye; Rodney Dawson; Andreas H Diacon; Madeline Hanekom; Timothy D McHugh; Carl M Mendel; Sarah K Meredith; Michael E Murphy; Saraswathi E Murthy; Andrew J Nunn; Patrick P J Phillips; Kasha P Singh; Melvin Spigelman; Genevieve H Wills; Stephen H Gillespie Journal: BMC Infect Dis Date: 2018-07-11 Impact factor: 3.090