bias in research

Bias in research

Good research is about evidence-based decision making

Good research has always been about evidence. Reliable evidence. The bias in research node aims to ensure that research, and related policy decisions, rest on strong and unbiased pillars of evidence.

Our vision is that all researchers understand and mitigate risks of bias in research, so health policies based on these findings are founded on strong pillars of evidence. The Charles Perkins Centre is the perfect place to collaborate with content area experts to achieve this goal.

Obesity, diabetes and cardiovascular disease are complex conditions because they result from a mix of biological, social, cultural and other factors. Evidence-based decisions are needed to prevent and treat these conditions, but scientists, methodologists and policy makers can’t work in isolation to tackle these complex issues.

We use quantitative and qualitative methods to examine experimental bias, and investigate other factors that influence design, conduct and publication of human and animal research. Part of our work involves raising awareness of the lack of research reproducibility and transparency, and poor reporting of research.

Researchers in this Node are also involved in designing and testing rigorous interventions to:

  • reduce bias in research
  • promote research integrity
  • improve the uptake of research in the policy process.

As part of the Evidence, Influence and Policy Collaborative (EPIC) Research Group, this node has more than 30 local and international members.

Our research is already having an impact.

  • We have exposed bias and conflicts of interest in health-related research, including in the areas of pharmaceutical, environmental, nutrition, and tobacco control.
  • We have worked with diverse communities (e.g. journalists, lawyers, judges, and consumers) to increase their skills in evaluating bias in research.
  • Our work is being used to generate new gold standards for synthesising evidence for a wide variety of health policy decisions.
  • Our research has generated international efforts to advance methods for assessing bias and conducting systematic reviews in new areas (e.g. environmental risk assessment and complex public health questions).
  • Our research forms the basis for a number of collaborative efforts among governments and researchers to develop empirically based tools for assessing bias in research. Our methodology for assessing bias also supports agencies such as the NHMRC and the World Health Organization.
  •  The impact of our work on detecting bias in research is the increased recognition that selective reporting of research outcomes and whole studies makes it impossible to identify data for systematic reviews. This work has led to international reforms to increase data accessibility, to report conflicts of interest and fund more transparent research, and to calls for stricter standards and policies to manage conflicts of interest, critique and reporting evidence, and conduct systematic reviews.


  • University of Sydney – Utrecht Partnership Collaboration Awards (2018), An investigation into pre-clinical trial registration – On the path to enhancing best practice methodology in the use of animals for scientific purposes.
  • NHMRC project grant: APP1139997, Strengthening the evidence foundation for public health guidelines


  • Percie du Sert, N., et al (2020). The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research. PLOS Biology
  • Percie du Sert, N., et al (2020). Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLOS Biology
  • Percie du Sert, N., et al (2018). Revision of the ARRIVE guidelines: Rationale and scope. BMJ Open Science 2:e000002 doi:10.1136/bmjos-2018-00000002.
  • Created and launched a series on Commercial Influences in Health in the British Medical Journal.  The series includes a call to action, a way forward in terms of independence and transparency, and includes several research articles from node members.

Systematic review

  • Conducted a systematic review to identify tools for assessing bias in observational studies; and created an interactive interface to help researchers identify a tool.  This project was done in collaboration with NHMRC and WHO, is published in the NHMRC Guidelines for Guidelines (see below) and was recently accepted for publication in Environment International.


  • We participated in the Working Group that developed the ARRIVE guidelines 2.0: Updated guidelines for reporting animal research released in July 2020. (
  • Members of our team contributed to the discourse published in Nature on the role of animal study registries to reduce bias (Baker, M. Volume 573: 297-298. Nature  2019). (
  • We hosted members of the Radboud UMC Systematic Review Center for laboratory animal Experimentation (SYRCLE) to conduct workshops in the Charles Perkins Centre (November 2018) for animal researchers wanting to learn how to undertake systematic reviews.

Internal collaborators

External collaborators

  • Dr Jon Jureidini, University of Adelaide
  • Professor Mark Lawrence, Health, Deakin University
  • Professor Joel Lexchin, York University
  • Dr Daniele Mandrioli, John Hopkins University
  • Gabriel Axel Montes, University of Newcastle
  • Professor Nicolas Rasmussen, University of New South Wales
  • Dr Gyorgy Scrinis, University of Melbourne
  • Dr Anne Springer, University of Saskatchewan
  • Dr Anna Stoklosa, NHMRC Clinical Trials Centre
  • Associate Professor Luke Wolfenden, University of Newcastle

Project Node Leader

Dr Joanna Diong

Project Node Leader

A/Professor Kieron Rooney
Associate Professor Kieron Rooney
Visit A/Professor Rooney's profile