← The Peter Attia Drive

#143 - John Ioannidis, M.D., D.Sc.: Why most biomedical research is flawed, and how to improve it

Jan 4, 2021 1h 52m 23 insights
<div>John Ioannidis is a physician, scientist, writer, and a Stanford University professor who studies scientific research itself, a process known as meta-research. In this episode, John discusses his staggering finding that the majority of published research is actually incorrect. Using nutritional epidemiology as the poster child for irreproducible findings, John describes at length the factors that play into these false positive results and offers numerous insights into how science can course correct. </div> <div> </div> <div>We discuss:<br /> <ul> <li>John's background, and the synergy of mathematics, science, and medicine (2:40);</li> <li>Why most published research findings are false (10:00);</li> <li>The bending of data to reach 'statistical significance,' and the how bias impacts results (19:30);</li> <li>The problem of power: How over- and under-powered studies lead to false positives (26:00);</li> <li>Contrasting nutritional epidemiology with genetics research (31:00);</li> <li>How to improve nutritional epidemiology and get more answers on efficacy (38:45);</li> <li>How pre-existing beliefs impact science (52:30);</li> <li>The antidote to questionable research practices infected with bias and bad incentive structures (1:03:45);</li> <li>The different roles of public, private, and philanthropic sectors in funding high-risk research that asks the important questions (1:12:00);</li> <li>Case studies demonstrating the challenge of epidemiology and how even the best studies can have major flaws (1:21:30);</li> <li>Results of John's study looking at the seroprevalence of SARS-CoV-2, and the resulting vitriol revealing the challenge of doing science in a hyper-politicized environment (1:31:00);</li> <li>John's excitement about the future (1:47:45); and</li> <li>More.</li> </ul> <p>Learn more: <a href="https://peterattiamd.com/">https://peterattiamd.com/</a><br /> <br /> Show notes page for this episode: <a href="https://peterattiamd.com/JohnIoannidis">https://peterattiamd.com/JohnIoannidis</a> <br /> <br /> Subscribe to receive exclusive subscriber-only content: <a href="https://peterattiamd.com/subscribe/">https://peterattiamd.com/subscribe/</a><br /> <br /> Sign up to receive Peter's email newsletter: <a href="https://peterattiamd.com/newsletter/">https://peterattiamd.com/newsletter/</a><br /> <br /> Connect with Peter on <a href="http://Facebook.com/PeterAttiaMD"><u>Faceboo</u></a><u>k</u> | <a href="http://Twitter.com/PeterAttiaMD"><u>Twitter</u></a> | <a href="http://Instagram.com/PeterAttiaMD"><u>Instagram</u></a>.</p> </div>
Actionable Insights

1. Embrace Not Knowing

Actively seek out what you don’t know and be open to correcting previous understandings, as this continuous learning and self-correction are fundamental for scientific and human progress.

2. Prioritize Clinical Significance

When evaluating medical interventions or research findings, focus on whether an action makes a meaningful difference to a patient or community, rather than solely on statistical significance.

3. Adopt Genetics Research Model

For fields like epidemiology, conduct very large studies, analyze all relevant factors, join forces with other researchers to share data and protocols, and standardize analysis to maximize power, transparency, and credibility.

4. Pre-specify & Register Hypotheses

For randomized trials and other research, carefully pre-specify and register your hypotheses and protocols, ensuring they address clinically important and meaningful effect sizes, to establish transparent rules for the study.

5. Be Transparent About Biases

Recognize that scientists are human and have beliefs; strive to map these beliefs, be transparent about them, and actively restrain their influence on research conduct and interpretation to maintain objectivity.

6. Prioritize Experimental Evidence

When seeking to establish causality, prioritize obtaining experimental evidence, such as well-designed randomized trials, as this is the most reliable approach.

7. Use Stringent P-Value Thresholds

In scientific fields, particularly those with high multiplicity, consider using a more stringent p-value threshold (e.g., 0.005 or 10^-8) instead of the traditional 0.05 to reduce false positives.

8. Avoid Underpowered Studies

Do not conduct small, underpowered studies, as they are likely to produce false positive results or grossly exaggerate the magnitude of any detected signals.

9. Be Cautious with Big Data

When working with large datasets, be aware that studies can be overpowered, leading to statistically significant results that lack clinical meaning and may simply be measuring bias.

10. Implement Safeguards Against Self-Deception

Actively impose safeguards throughout the research process to minimize the chances of fooling yourself, which is the most important rule in science for maintaining honesty and integrity.

11. Conduct Exposure-Wide Association Testing

Instead of reporting on one nutrient or exposure at a time, analyze all collected exposures and outcomes simultaneously, reporting results that account for multiplicity and correlation, to achieve a more transparent and complete picture.

12. Utilize Mendelian Randomization

Consider using Mendelian randomization studies, which leverage genetic instruments, to create designs that are functionally equivalent to randomized trials, thereby enhancing the credibility of signals from observational research.

13. Verify with Multiple Analyst Teams

To enhance the credibility of research findings, especially in fields like nutrition, employ two or three independent analyst teams to analyze the same data and confirm consistent results.

14. Interpret Bradford Hill Cautiously

When applying Austin Bradford Hill’s criteria for causality, do so with temperance and caution, understanding that none of them are bulletproof rules and that nature’s operations can be unpredictable.

15. Consider Real-World Adherence

When evaluating dietary or other interventions, recognize that a diet or treatment is not truly better if people cannot adhere to it in real-life circumstances, as adherence is part of effectiveness.

16. Conduct Controlled Efficacy Trials

To gain insights into efficacy, run randomized trials under very controlled, supervised circumstances (e.g., in a clinic) with stringent monitoring of diet and physiological responses, to understand optimal treatment under ideal conditions.

17. Advocate for Better Research Incentives

Work to realign incentives in science away from producing ‘striking’ results from small studies and towards supporting high-risk, definitive research, even if it has a high chance of failing, to truly advance knowledge.

18. Communicate Science Honestly

Communicate scientific findings to the public with honesty, clarity, and without exaggerated promises, defending scientific methods and principles against non-scientific claims and populist attacks.

19. Protect Scientists from Attacks

Advocate for protecting scientists from personal attacks and vitriol, ensuring they can disseminate their objective scientific findings without fear of reprisal, especially in highly polarized environments.

20. Learn Mathematics

Cultivate a love for and understanding of mathematics, as it forms the foundation for many scientific endeavors and can transform approaches to complex questions, enabling significant progress.

21. Combine Rigorous Science & Quantitative Tools

In medicine and other fields, integrate rigorous scientific methods with quantitative approaches and tools to obtain reliable evidence and make a meaningful difference for human beings.

22. Continuously Reinvent Self as Scientist

Embrace the role of a scientist as a continuous process of reinvention, constantly searching for new frontiers, new questions, and new ways to correct errors and improve upon previous understanding.

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