Xiang-Lin Tan1, David M Kern1, M Soledad Cepeda2. 1. Janssen Research & Development, LLC, Titusville, NJ, USA. 2. Janssen Research & Development, LLC, Titusville, NJ, USA. scepeda@its.jnj.com.
Abstract
BACKGROUND: An important component of a systematic strategy for safety surveillance is prospective identification of anticipated serious adverse events (SAEs). Developing a structured approach to identify anticipated events and estimating their incidence can help align the safety strategy and the safety surveillance efforts. METHODS: We developed a novel approach to identify anticipated events for a hypothetical randomized, double-blind, controlled trial in subjects with bipolar disorder using the adverse events reported in the placebo arm of trials from the ClinicalTrials.gov database. We searched the ClinicalTrials.gov database for all trials on bipolar depression with similar inclusion/exclusion criteria and study duration as our hypothetical study. The frequencies of anticipated events in placebo arms were abstracted from each trial and 95% confidence intervals (CI) were calculated using the Clopper-Pearson method. Meta-analysis with a random effects model was performed to obtain a summary estimate and 95% CI for the events identified in more than one trial. RESULTS: A total of 129 clinical trials were initially identified, and 18 were ultimately selected as they met all the selection criteria. There were 69 unique anticipated SAEs identified, and 13 out of 69 were reported in at least 2 clinical trials. The top 5 anticipated SAEs for our study were: (1) hospitalization, psychiatric symptom (3.57%); (2) suicidal behavior, overdose (3.57%), (3) cholecystitis (2.86%); (4) fall (2.86%); (5) road traffic accident, injury (2.86%). CONCLUSION: We successfully identified the anticipated events from registered trials that included a population similar to our trial. This method for identifying anticipated events could be applied to other disease areas.
BACKGROUND: An important component of a systematic strategy for safety surveillance is prospective identification of anticipated serious adverse events (SAEs). Developing a structured approach to identify anticipated events and estimating their incidence can help align the safety strategy and the safety surveillance efforts. METHODS: We developed a novel approach to identify anticipated events for a hypothetical randomized, double-blind, controlled trial in subjects with bipolar disorder using the adverse events reported in the placebo arm of trials from the ClinicalTrials.gov database. We searched the ClinicalTrials.gov database for all trials on bipolar depression with similar inclusion/exclusion criteria and study duration as our hypothetical study. The frequencies of anticipated events in placebo arms were abstracted from each trial and 95% confidence intervals (CI) were calculated using the Clopper-Pearson method. Meta-analysis with a random effects model was performed to obtain a summary estimate and 95% CI for the events identified in more than one trial. RESULTS: A total of 129 clinical trials were initially identified, and 18 were ultimately selected as they met all the selection criteria. There were 69 unique anticipated SAEs identified, and 13 out of 69 were reported in at least 2 clinical trials. The top 5 anticipated SAEs for our study were: (1) hospitalization, psychiatric symptom (3.57%); (2) suicidal behavior, overdose (3.57%), (3) cholecystitis (2.86%); (4) fall (2.86%); (5) road traffic accident, injury (2.86%). CONCLUSION: We successfully identified the anticipated events from registered trials that included a population similar to our trial. This method for identifying anticipated events could be applied to other disease areas.
Authors: Phil Bonomi; Nina Stuccio; C J Delgra; Meredith K Chuk; Alexander Spira; Anne C Deitz; Gideon M Blumenthal; Andrea Ferris; Yutao Gong; Jinghua He; Upal Basu Roy; Wendy Selig Journal: Ther Innov Regul Sci Date: 2020-03-26 Impact factor: 1.778