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A Retrospective Analysis of Clinical Research Misconduct Using FDA-Issued Warning Letters and Clinical Investigator Inspection List From 2010 to 2014

医学 食品药品监督管理局 临床试验 编码(社会科学) 临床研究 预警系统 医疗急救 医学物理学 统计 计算机科学 病理 数学 电信
作者
Christopher A. Romano,Singh Nair,Ellise Delphin
出处
期刊:Anesthesia & Analgesia [Ovid Technologies (Wolters Kluwer)]
卷期号:126 (3): 976-982 被引量:3
标识
DOI:10.1213/ane.0000000000002694
摘要

The US Food and Drug Administration (FDA) conducts inspections of clinical investigation sites as a component of clinical trial regulation. The FDA describes the results of these inspections in the Clinical Investigator Inspection List (CLIIL). More serious violations are followed up in FDA warning letters issued to investigators. The primary objective of the current study is to qualitatively and quantitatively describe the CLIIL data and contents of FDA-issued warning letters from 2010 to 2014.We retrospectively analyzed the CLIIL and FDA warning letters. For the CLIIL, we quantified the frequency of each violation among other data points. We compared recent data (2010-2014) to the previous 5 years (2005-2009). To analyze FDA warning letters, we developed a coding system to quantify the frequency of violations found.We analyzed 3637 inspections in the CLIIL database and 60 warning letters. Overall, there was a decrease or no change in all violations in the CLIIL database. The largest violation code reported was "failure to follow investigational plan" in both the 2005-2009 and 2010-2014 timeframes. Coding of FDA warning letters shows that the most common violations reported were failing to maintain accurate case histories (10.82%), enrolling ineligible subjects (8.85%), and failing to perform required tests (8.52%).The overall decrease in violations is encouraging. But, the high proportion of violations related to failure to follow the investigational plan is concerning as the complexity of trials increases. We conclude that more detailed information is necessary to accurately evaluate these violations. The current study provides a model for creating more granular data of violations to better inform clinical investigators and improve clinical trials.
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