Dataset Information

Physician-Reported Experience and Understanding of Adverse Event Attribution in Cancer Clinical Trials.

ABSTRACT:

Objectives

To report the results of a survey conducted among Mayo Clinic medical oncologists, hematologists, and cancer prevention specialists to better understand the current practice of determining whether an adverse event that a patient experience in a clinical trial is related to the drug under investigation, a process commonly known as attribution, as well as to formulate recommendations for an improved system.

Patients and methods

An electronic survey was developed and conducted (from August 2 through 29, 2017) among 165 medical oncologists, hematologists, and cancer prevention specialists at the 3 Mayo Clinic sites: Rochester, Minnesota; Scottsdale, Arizona; and Jacksonville, Florida. The survey included 21 items that queried clinicians about their clinical practice and trial experience, their training and process in adverse event attribution assignment, and their recommendations for improving the current attribution system.

Results

Thirty-seven percent (61 of 165) of physicians responded to the survey. The median number of years in clinical practice was 15 (range, 1-64) and that of clinical trial experience 12. Eighty-nine percent (54 of 61) had served as a trial principal investigator. Only 15% (9 of 60) of responders reported having received any formal attribution training. Eighty percent (48 of 60) were confident about their ability to assign attribution. Seventy-five percent (45 of 60) consulted their colleagues or study chair when assigning attribution. Sixty-seven percent (40 of 60) recommended formal training to improve attribution accuracy.

Conclusion

Very few clinical trialists in our survey received any formal training for adverse event attribution, yet most identified formal training as effective means to improve attribution accuracy. These data underscore an unmet need of formal adverse event attribution training among clinical trialists.

SUBMITTER: Le-Rademacher JG

PROVIDER: S-EPMC6543498 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Publications

Physician-Reported Experience and Understanding of Adverse Event Attribution in Cancer Clinical Trials.

Le-Rademacher Jennifer G JG Storrick Elizabeth M EM Jatoi Aminah A Mandrekar Sumithra J SJ

Mayo Clinic proceedings. Innovations, quality & outcomes 20190527 2

<h4>Objectives</h4>To report the results of a survey conducted among Mayo Clinic medical oncologists, hematologists, and cancer prevention specialists to better understand the current practice of determining whether an adverse event that a patient experience in a clinical trial is related to the drug under investigation, a process commonly known as attribution, as well as to formulate recommendations for an improved system.<h4>Patients and methods</h4>An electronic survey was developed and condu ...[more]

PMID: 31193907

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Project description:INTRODUCTION:Adverse event (AE) under-reporting has been a recurrent issue raised during health authorities Good Clinical Practices (GCP) inspections and audits. Moreover, safety under-reporting poses a risk to patient safety and data integrity. The current clinical Quality Assurance (QA) practices used to detect AE under-reporting rely heavily on investigator site and study audits. Yet several sponsors and institutions have had repeated findings related to safety reporting, and this has led to delays in regulatory submissions. Recent developments in data management and IT systems allow data scientists to apply techniques such as machine learning to detect AE under-reporting in an automated fashion. OBJECTIVE:In this project, we developed a predictive model that enables Roche/Genentech Quality Program Leads oversight of AE reporting at the program, study, site, and patient level. This project was part of a broader effort at Roche/Genentech Product Development Quality to apply advanced analytics to augment and complement traditional clinical QA approaches. METHOD:We used a curated data set from 104 completed Roche/Genentech sponsored clinical studies to train a machine learning model to predict the expected number of AEs. Our final model used 54 features built on patient (e.g., demographics, vitals) and study attributes (e.g., molecule class, disease area). RESULTS:In order to evaluate model performance, we tested how well it would detect simulated test cases based on data not used for model training. For relevant simulation scenarios of 25%, 50%, and 75% under-reporting on the site level, our model scored an area under the curve (AUC) of the receiver operating characteristic (ROC) curve of 0.62, 0.79, and 0.92, respectively. CONCLUSION:The model has been deployed to evaluate safety reporting performance in a set of ongoing studies in the form of a QA/dashboard cockpit available to Roche Quality Program Leads. Applicability and production performance will be assessed over the next 12-24 months in which we will develop a validation strategy to fully integrate our model into Roche QA processes.

Dataset Information

Physician-Reported Experience and Understanding of Adverse Event Attribution in Cancer Clinical Trials.

Objectives

Patients and methods

Results

Conclusion

Publications

Physician-Reported Experience and Understanding of Adverse Event Attribution in Cancer Clinical Trials.

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