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July 12, 2022
Reproducibility in medical research
Ewout W. Steyerberg, PhD
Professor of Clinical Biostatistics and
Medical Decision Making
Dept of Biomedical Data Sciences
Leiden University Medical Center
Thanks to many for assistance and inspiration
Disclaimer
12-Jul-22
2 Insert > Header & footer
As a biostatistician, I like numbers
Reproducibility and p-values
12-Jul-22
3 Insert > Header & footer
Reproducibility and p-values
12-Jul-22
4 Insert > Header & footer
Reproducibility crisis
• Fabio tekst:
Science journalists can help citizens to notice not only [..]
the scandals involving lack of integrity and bad methodology,
but also the [..] initiatives that are trying to fix the current system of
incentives/disincentives that does not value reproducibility studies and
instead often leaves room for rushed, hyped, unreliable research.
1. Rushed / hyped
2. Unreliable
3. Value reproducibility
1. Rushed &hyped research: COVID
12-Jul-22
6 Insert > Header & footer
12-Jul-22
7 Insert > Header & footer
Hyped research: COVID risk prediction
12-Jul-22
8 Insert > Header & footer
Conclusion 1
Rushed & hyped research happens
• Medical context: urgency
• Reward context: newness
12-Jul-22
9 Insert > Header & footer
2. Unreliable research
“Research reliability is the degree to which research methods produces stable
and consistent results”
Psychology:
test-retest / parellel forms / inter-rater / internal consistency reliability
Epidemiology / biostatistics:
Bias + variance
12-Jul-22
10 Insert > Header & footer
Bias & variance
Reduce variance
• Increase sample size
• Better measurement
 Beware of ““Absence of evidence is not evidence of absence”
Reduce bias
• Strong study design costly
• Big Data everywhere
 Beware of overinterpretation
12-Jul-22
11 Insert > Header & footer
Conclusion 2
Unreliable research happens
• Ethics: “not too many patients”
• Resources: limited  “efficient research”
12-Jul-22
12 Insert > Header & footer
12-Jul-22
13 Insert > Header & footer
Prediction models
Development – validation – impact assessment / implementation
3. More value on reproducibility
Incentives in medical research
12-Jul-22
14 Insert > Header & footer
Some key principles
• Contribute to societal needs
• Publish completely and transparently, regardless of the results
• Facilitate dissemination and use of research data and results by others
12-Jul-22
15 Insert > Header & footer
Conclusion 3
Incentives need to change
• Classic: publish many papers in high IF journals
• Modern: registered reports; share data; share reproducible results;
collaborative research; peer review;
12-Jul-22
16 Insert > Header & footer
12-Jul-22
17 Insert > Header & footer

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Reproducibility Leiden 12jul22.pptx

  • 1. July 12, 2022 Reproducibility in medical research Ewout W. Steyerberg, PhD Professor of Clinical Biostatistics and Medical Decision Making Dept of Biomedical Data Sciences Leiden University Medical Center Thanks to many for assistance and inspiration
  • 2. Disclaimer 12-Jul-22 2 Insert > Header & footer As a biostatistician, I like numbers
  • 3. Reproducibility and p-values 12-Jul-22 3 Insert > Header & footer
  • 4. Reproducibility and p-values 12-Jul-22 4 Insert > Header & footer
  • 5. Reproducibility crisis • Fabio tekst: Science journalists can help citizens to notice not only [..] the scandals involving lack of integrity and bad methodology, but also the [..] initiatives that are trying to fix the current system of incentives/disincentives that does not value reproducibility studies and instead often leaves room for rushed, hyped, unreliable research. 1. Rushed / hyped 2. Unreliable 3. Value reproducibility
  • 6. 1. Rushed &hyped research: COVID 12-Jul-22 6 Insert > Header & footer
  • 7. 12-Jul-22 7 Insert > Header & footer
  • 8. Hyped research: COVID risk prediction 12-Jul-22 8 Insert > Header & footer
  • 9. Conclusion 1 Rushed & hyped research happens • Medical context: urgency • Reward context: newness 12-Jul-22 9 Insert > Header & footer
  • 10. 2. Unreliable research “Research reliability is the degree to which research methods produces stable and consistent results” Psychology: test-retest / parellel forms / inter-rater / internal consistency reliability Epidemiology / biostatistics: Bias + variance 12-Jul-22 10 Insert > Header & footer
  • 11. Bias & variance Reduce variance • Increase sample size • Better measurement  Beware of ““Absence of evidence is not evidence of absence” Reduce bias • Strong study design costly • Big Data everywhere  Beware of overinterpretation 12-Jul-22 11 Insert > Header & footer
  • 12. Conclusion 2 Unreliable research happens • Ethics: “not too many patients” • Resources: limited  “efficient research” 12-Jul-22 12 Insert > Header & footer
  • 13. 12-Jul-22 13 Insert > Header & footer Prediction models Development – validation – impact assessment / implementation 3. More value on reproducibility
  • 14. Incentives in medical research 12-Jul-22 14 Insert > Header & footer
  • 15. Some key principles • Contribute to societal needs • Publish completely and transparently, regardless of the results • Facilitate dissemination and use of research data and results by others 12-Jul-22 15 Insert > Header & footer
  • 16. Conclusion 3 Incentives need to change • Classic: publish many papers in high IF journals • Modern: registered reports; share data; share reproducible results; collaborative research; peer review; 12-Jul-22 16 Insert > Header & footer
  • 17. 12-Jul-22 17 Insert > Header & footer