Identifying Anticipated Events of Future Clinical Trials by Leveraging Data from the Placebo Arms of Completed Trials
| Autor: | M. Soledad Cepeda, David M. Kern, Xiang-Lin Tan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
medicine.medical_specialty
Bipolar disorder Population 030204 cardiovascular system & hematology Placebo law.invention 03 medical and health sciences 0302 clinical medicine Clinical trials Randomized controlled trial Double-Blind Method law Medicine Humans Pharmacology (medical) 030212 general & internal medicine Prospective Studies education Adverse effect Pharmacology Toxicology and Pharmaceutics (miscellaneous) Depression (differential diagnoses) Original Research Safety report Randomized Controlled Trials as Topic Anticipated events education.field_of_study business.industry Public Health Environmental and Occupational Health medicine.disease Confidence interval Clinical trial Hospitalization Adverse events Emergency medicine business |
| Zdroj: | Therapeutic Innovation & Regulatory Science |
| ISSN: | 2168-4804 2168-4790 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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