A Data-Driven Guide to Get Your Clinical Research Retracted
12th October 2022 By MARS Research Hub

A Data-Driven Guide to Get Your Clinical Research Retracted

A retracted paper is a paper that has been formally withdrawn by the journal that published it. The term is typically used in academic publishing to refer to articles published in peer-reviewed journals and then later retracted.

The reasons for retracting a paper are usually because of ethical or data integrity issues, such as plagiarism, data manipulation, or duplicate publication.

The process of retracting a paper can be initiated by the journal's author, reviewer, or editor-in-chief.

There are many reasons one might get their clinical paper retracted after publication. One reason is if there was an error in data when the accuracy or validity of the data is questionable, and this can quickly occur unintentionally.

Reason #1 – Data dredging

Data dredging is a type of scientific misconduct that involves selectively analyzing data to find an interesting result.

A typical example of data dredging is when researchers conduct a study and then analyze their results for the factors that they think are most likely to affect the outcome.

The problem with this kind of research is that it can lead to false conclusions about the factors that affect a study's outcome. For example, if researchers only look for associations between high blood pressure and heart disease in men, they may find one because more men are in the study than women. But if they had looked at both sexes, they would have found no association because more women were in the study than men.

Reason #2 – Fabricated Data

Data fabrication is a deliberate act of making or falsifying data to deceive others.

Clinical trials are a crucial part of the medical research and development process. They are vital for testing new treatments and finding out whether they work. Data fabrication in clinical research can have catastrophic consequences, such as halting clinical trials, discrediting the results, or even leading to death.

There are many motivations behind fabricating data in clinical research. Some people may want to advance their careers by publishing more papers and getting more funding. Some may want to cover up mistakes they made during the experiment to maintain their reputation. Others may be pressured by their supervisors or colleagues who wish to fabricate data so they can meet deadlines or get published in prestigious journals and conferences.

Reason #3 – A mistake made in the data, either in data entry, gathering, or identification

Data duplication is a significant issue in clinical research. It can lead to inaccurate data and duplicated patients.

A mistake made in data entry, gathering, or identification is the most common reason for data duplication. These mistakes can be made by the researcher, the staff who gather the data, or by a wrong keystroke when entering it into the database.

In clinical research, there are many cases of patient duplication where one patient has been entered more than once into a database. The same applies to false date records where someone enters incorrect information about when an event has happened or when an observation has been taken.

Clinical data management skills can help you avoid data duplication and errors in data entry using electronic case report forms and applying the programming skills in R to easily solve all data problems in no time.

Clinical data management skills are essential for health care professionals to ensure that they are not duplicating the data entry and there are no errors.

The clinical data management skills include:

- Data Entry: The process of entering medical data into a computer system with a pre-defined set of variables and levels

- Clinical Data Management: Managing medical records, including designing, implementing, and supporting systems that store and manipulate patient information.

- Electronic Case Report Forms (eCRFs): A form used by healthcare professionals to document the care provided to a patient during an office visit or hospitalization or a system used by Contract Research Organizations to collect data on clinical trials

- Clinical Documentation Improvement (CDI): The process of identifying, analyzing, and improving documentation practices in hospitals and other clinical research settings.