Simplifying eligibility criteria can cut data fabrication risk

By Nick Taylor

- Last updated on GMT

Related tags Clinical trial

Simplifying clinical trial eligibility criteria and reducing the number of recorded variables can cut risk of data fraud, statisticians said.

Falsification of clinical trial data can hinder drug development, damage reputations and, potentially, harm patients. Many efforts focus on detecting fraud but biopharm and CROs (contract research organisation) can take steps to decrease the likelihood of data falsification before a study even starts.

Two potentially successful preventive strategies are simplifying the eligibility criteria for the trial and reducing the numbers of variables being recorded​”, Scotland-based statisticians wrote in the journal Significance​.

In general the statisticians recommend focusing “on the data items that are most easily affected by fraud​”. Suggested items to focus on include adverse events, compliance data, patient diaries and eligibility criteria, such as the age, sex and medical history of patients.

Location can also help discourage data falsification. “In large clinical trials, embedding assessments as far as possible within routine clinical practice is one way to encourage adherence to the protocol by triallists​”, the statisticians wrote.

Although industry could do more to prevent fraud reported cases of data fabrication are rare. Of the 22 trials recorded in the UK ethics review database from 1997 to 2011, three relate to potential data fabrication.

Also, the impact of fraud is minimised by randomisation and blinding, meaning the conclusions of a trial can still be valid. The exception is when the “fraud subverts the randomisation process or the blinded assessment of patient outcomes​”, the authors wrote.

Detecting fraud

Detecting fraud can be as simple as spotting different handwriting in patient diaries but statistical approaches are also used. It is very hard “to make a set of several interrelated measurements look genuine​”, the authors write, and this makes it possible to detect data fabrication.

Conventional statistical analysis may focus on outlying data that can have excessive influence over conclusions. However, when detecting data fraud data points that are too close to the mean are also of interest as those fabricating findings are likely to choose values that appear feasible.

Related news

Show more

Related products

show more

Using Define-XML to build more efficient studies

Using Define-XML to build more efficient studies

Content provided by Formedix | 14-Nov-2023 | White Paper

It is commonly thought that Define-XML is simply a dataset descriptor: a way to document what datasets look like, including the names and labels of datasets...

Why should you use clinical trial technology?

Why should you use clinical trial technology?

Content provided by Formedix | 01-Nov-2023 | White Paper

New, innovative clinical trial technology is helping to revolutionize the research landscape. COVID-19 demonstrated that clinical trials can be run much...

Overcoming rapid growth challenges with process liquid preparation

Overcoming rapid growth challenges with process liquid preparation

Content provided by Thermo Fisher Scientific - Process Liquid Preparation Services | 01-Nov-2023 | Case Study

A growing contract development manufacturing organization (CDMO) was challenged with the need to quickly expand their process liquid and buffer preparation...

Related suppliers

Follow us


View more