Is omitting a complication incidence during research data analysis an example of falsification of data?

Research Misconduct: Falsification of Data

The scenario described represents falsification of data, which is a form of research misconduct that involves manipulating research materials, equipment, or processes, or changing or omitting data such that the research is not accurately represented in the research record. 1

Definition and Classification

Falsification is distinct from fabrication and plagiarism, forming the classic triad of research misconduct (FFP). 2

• Fabrication involves making up data or results that never existed 3, 4
• Falsification involves manipulating, changing, or omitting data or results to misrepresent the research 3, 4
• Plagiarism involves using someone else's work without proper attribution 2

The action described—deliberately excluding a complication that actually occurred and adjusting the data "as if nothing happened"—constitutes falsification because it involves omitting real data to misrepresent the research findings. 4, 2

Why This Constitutes Falsification

Omitting actual complication data fundamentally betrays scientific truth and undermines the integrity of the research record. 2

• The researcher is changing the dataset by removing real events that occurred during the study 1
• This manipulation misrepresents the true complication rate and safety profile of the intervention being studied 3
• The action appears intentional and deliberate, which is a key criterion for research misconduct 5

Clinical and Ethical Implications

This type of falsification can directly harm future patients and undermine public trust in clinical research. 3

• Patients enrolled in future trials may be exposed to undisclosed risks based on falsified safety data 3
• Clinical decision-making by physicians may be compromised when based on inaccurate complication rates 1
• Resource allocation and health policy decisions may be misdirected by erroneous data 6
• The misconduct damages the reputation of the scientific community and erodes public confidence in research 1, 3

Common Pitfalls in Complication Reporting

While the scenario describes intentional falsification, researchers should be aware that legitimate methodological challenges exist in complication reporting: 1

• Missing data should be reported transparently, not simply excluded 1
• Definitional variability in complications requires clear specification, not selective omission 1
• Publication bias may favor low complication rates, but this does not justify data manipulation 1

The key distinction is transparency: legitimate research reports missing or incomplete data honestly, while falsification involves deliberate concealment or manipulation. 1