Institute of Bioinformatics and Applied Biotechnology (IBAB), Bangalore, Karnataka, INDIA.
Indian J Med Ethics. 2024 Jan-Mar;IX(1):11-18. doi: 10.20529/IJME.2023.071.
In recent years, there has been a big push to register trials, but there are a number of problems with the data in public clinical trial registries. Here, we describe a cross-sectional study of the classification of the primary sponsors of all Phase 2, Phase 2/3, and Phase 3 interventional trials registered with the Clinical Trials Registry-India between May 15, 2016 and May 14, 2021.
Data was scraped from the records of CTRI, various filters were applied, and the trials of interest identified.
Of 5,453 trials, 105 did not identify a sponsor and 1,080 were sponsored by individuals. Of the remaining 4,268 trials, 427 had unique sponsors, and 3,841 had a total of 350 non-unique sponsors. Of the 350 sponsors, 202 were classified in a single category, and 147 were classified in two or more categories. Overall, of the 3,841 trials, sponsors in 3,537 (92.1%) were classified in one or more of nine well-defined categories, and 304 (7.9%) were classified as various versions of "Other". Three major problems with the sponsor data were identified: each trial does not necessarily list a sponsor, a given sponsor may be categorised in multiple ways, and there has been an excessive use of the "Other" category. Addressing these problems will enable automated analyses of the database, and improve the transparency of the data.
Our study generates evidence highlighting the need to improve the trial registration system in India, and perhaps elsewhere.
近年来，人们大力推动试验注册，但公共临床试验注册库中的数据存在诸多问题。在这里，我们描述了一项横断面研究，该研究对 2016 年 5 月 15 日至 2021 年 5 月 14 日期间在临床试验注册印度中心（Clinical Trials Registry-India，CTRI）注册的所有 2 期、2/3 期和 3 期干预试验的主要发起者的分类进行了研究。
从 CTRI 的记录中提取数据，应用各种过滤器，并确定感兴趣的试验。
在 5453 项试验中，有 105 项未确定赞助商，1080 项由个人赞助。在其余的 4268 项试验中，有 427 项有独特的赞助商，3841 项有 350 个非独特的赞助商。在 350 个赞助商中，有 202 个被归为单一类别，有 147 个被归为两个或更多类别。总的来说，在 3841 项试验中，有 3537 项（92.1%）的赞助商被归入九个明确定义的类别中的一个或多个类别，有 304 项（7.9%）被归入各种版本的“其他”类别。赞助商数据存在三个主要问题：每个试验不一定列出一个赞助商，一个给定的赞助商可能被归类为多种方式，并且“其他”类别被过度使用。解决这些问题将使数据库的自动化分析成为可能，并提高数据的透明度。
我们的研究提供了证据，强调需要改进印度的试验注册系统，也许在其他地方也是如此。
