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Excursions into the garden of the forking paths
- 1. EXCURSIONS INTO THE GARDEN OF THE FORKING PATHS P-VALUE FETISHISATION, REPLICATION CRISIS, AND THE TENSION BETWEEN INNOVATION AND CONFIRMATION http://bit.ly/helmholtzdirnagl
- 2. Personal motivation: Decades of futile translational stroke research • Millions of animals killed • Hundreds (thousands?) of neutral or negative clinical trials • Thousands of researchers and clinicians globally • Many billions spent on preclinical research ?
- 3. Take home I: The garden of the forking paths http://bit.ly/2q2gtXqhttp://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf http://bit.ly/2JzblTR
- 4. Take home II No scientific progress without reproducibility failures To boldly go where no man… Exploration at low base rate Innovation ‚Paradigm shift‘ Incompetence Bad designs Tacit knowledge (bad reporting) Low validity (bias) Misconduct The Good The Bad Essential non-reproducibility (Kuhn) Detrimental non-reproducibility (Popper)
- 5. Taken home III Confirmation – weeding out the false positives of exploration Jonathan Kimmelman PLoS Biol. (2014) 12:e1001863.
- 6. >6000 cit. PLoS Med. 2005;2:e124
- 7. Modfied after Gary Larson Bias: Subjective reality informed by ones preferences
- 8. Macleod MR, et al. (2015) Risk of Bias in Reports of In Vivo Research: A Focus for Improvement. PLoS Biol 13: e1002273. Low prevalence of methods to prevent bias
- 9. Alzheimer's disease models models Blinded conduct of experiment Blinded assessment of outcome Blinded assessment of outcome Stroke models (NXY-095) Blinded assessment of behavioural outcome No Yes Improvementinbehaviouraloutcome (StandardisedEffectSize) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Blinded assessment of behavioural outcome No Yes Improvementinbehaviouraloutcome (StandardisedEffectSize) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Blinded assessment of behav No Improvementinbehaviouraloutcome (StandardisedEffectSize) 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Reductionininfarctsize Reductionininfarctsize > 30 studies > 500 animals Bias inflates effect sizes
- 10. PLoS Biol. 2016;14:e1002331 Effects of attrition in experimental biomedical research
- 11. PLoS Biol. 2016;14:e1002331 Bias produces false positives and inflates effect sizes
- 13. Overall median power of 730 primary neuroscience studies: 21 % Power failure in neuroscience!
- 15. Not knowing what is false and what is not, the researcher sees 95 hypotheses as true, 45 of which are not. α = 0.05; ß = 0.5
- 16. Mean group size n ≈ 8 Mean statistical power ≈ 45 % False positive rate (p ≤ 0.05): ≈ 50 % Overestimation of true effects: ≈ 50 % “Low sample size bias“ leads to false positives and effect size inflation
- 17. Beyond bias: HARKING – Hypothesizing after the results are known
- 18. http://xkcd.com/882/ Beyond bias: p - Hacking
- 20. In exploratory investigation, researchers should aim at generating robust pathophysiological theories of disease. Currently we often see a mixup of both modes. This prevents us from tailoring our study designs accordingly. In confirmatory investigation, researchers should aim at demonstrating strong and reproducible treatment effects in relevant animal models. Exploration vs Confirmation
- 21. Exploratory Confirmatory Hypothesis (+) +++ Establish pathophysiology +++ (+) Sequence and details of experiments established at onset (+) +++ Primary endpoint - ++ Sample size calculation (+) +++ Blinding +++ +++ Randomization +++ +++ External validity (aging, comorbidities, etc.) - ++ In/Exclusion criteria ++ +++ Test statistics + +++ Preregistration (-) +++ Sensitivity (Type II error) Find what might work ++ + Specificity (Type I error) Weed out false positives + +++ Stroke 2016; 47:2148-2153
- 22. PLoS Biol. (2014) 12:e1001863. Exploration vs Confirmation
- 24. ‚ .. non-reproducible single occurrences are of no significance to science …‘ The Logic of Scientific Discovery (1934) Sir Karl Popper (1902-1994) ‘We do not take even our own observations quite seriously, or accept them as scientific observations, until we have repeated and tested them. Only by such repetitions can we convince ourselves that we are not dealing with a mere isolated ‘coincidence’, but with events which, on account of their regularity and reproducibility, are in principle inter- subjectively testable.’
- 25. The lexicon of reproducibility Methods reproducibility: Same data, same tools, same results? Adds no additional evidence! Results reproducibility (aka „replication“): Technically competent repetition, i.e. a new study. Could be strict: identical conditions: or conceptual: altered conditions (does causal claim extend to previously unsampled settings?) Inferential reproducibility: Same conclusions from study replication or re-analysis? Not all scientists come to the same conclusions from same results, or may make different analytic choices. What is concluded or recommended from a study is often the only thing that matters! Adapted from Goodman et al. Sci Transl Med. 2016;8:341ps12.
- 26. What do we mean by 'reproducible'? Significance and P values: Evaluating replication effect against null hypothesis of no effect Evaluating replication effect against original effect size: Is the original effect size within the 95% CI of the effect size estimate from the replication. Alternatively: Comparing original and replication effect sizes Meta-analysis combining original and replication effects: Combining original and replication effect sizes for cumulative evidence Subjective assessment of “Did it replicate?” From the Open Science Collaboration, Psychology Replication, Science. 2015 ;349(6251):aac4716
- 27. A false dichotomy Replication Non-Replication
- 28. The emptiness of failed replication (?) Mitchell J (2014) On the evidentiary evidence of failed replication http://jasonmitchell.fas.harvard.edu/Papers/Mitchell_failed_science_2014.pdf
- 29. The emptiness of failed replication Does a failure to replicate mean that the original result was a false positive? Or was the failed replication a false negative? Does successful replication mean that the original result was correct? Or are both results false positives?
- 30. Hidden moderators - Contextual sensitivity – Tacit knowledge ‚We analyzed 100 replication attempts in psychology and found that the extent to which the research topic was likely to be contextually sensitive (varying in time, culture, or location) was associated with replication success. This relationship remained a significant predictor of replication success even after adjusting for characteristics of the original and replication studies that previously had been associated with replication success (e.g., effect size, statistical power).‘ Proc Natl Acad Sci. 2016;113:6454-9.
- 31. "Standardization fallacy": Low external validity, poor reproducibility Nat Methods. 2009;6:257-61.Trends Pharmacol Sci. 2016;37:509-10
- 32. p = 0.049 (p< α = 0.05) Assume that the experimental result is correct, i.e. measured difference equals (unknown) treatment effect. Repeat experiment under identical conditions (i.e. 'strict replication'). What is the probability to reproduce the significant findings? 50 %! How likely is strict replication ?
- 33. Replication failure as an indicator of cutting edge research? Dirnagl (2017) How likely are your hypotheses, really? https://dirnagl.com/2017/04/13/how-original-are-your-scientific-hypotheses-really/
- 35. The garden of the forking paths http://bit.ly/2q2gtXqhttp://www.stat.columbia.edu/~gelman/research/unpublished/p_hacking.pdf http://bit.ly/2JzblTR
- 36. Fig. 6y demonstrates… Brandt et al. Cell Metabolism 27, 2018, 118-135.e8
- 37. Resolving the tension: Discovery & Replication Suggested reading: Wagenmakers EJ, Dutilh G, Sarafoglou A. Perspect Psychol Sci. 2018 Jul;13(4):418-427 Chang and Eng Bunker circa 1865. Foto Hulton/Getty
- 38. No scientific progress without nonreproducibility To boldly go where no man… Exploration at low base rate Innovation ‚Paradigm shift‘ Incompetence Bad designs Tacit knowledge (bad reporting) Low validity (bias) Misconduct The Good The Bad Essential non-reproducibility (Kuhn) Detrimental non-reproducibility (Popper)
- 39. Reduce Bias! Use blinding, randomization,in/exclusion criteria. Report results according to guidelines (e.g. ARRIVE). Increase Power! Check your power. Achieve at least 80%. Do apriori sample size calculations. Probably you need to increase n‘s. Replicate. Use statistics sensibly! P-values do not provide evidence regarding a model or hypothesis. Test statistics are overrated (and overused) in exploration. Think biological significance, think effect size. Replicate. Practice Open Science Preregister. Publish NULL results. Make the original data available. Don’t get lost in the garden of the forking paths
Editor's Notes
- Lots of potential biases are lurking which may impact on our experimental results. From a very recent paper study from Malcolm's group we know that randomization, blinding, and conflict of interest statements are still not as prevalent as we might hope, in fact in preclinical stroke research below 40 %. I should mention that stroke research is not doing any worse than other fields in neuroscience, for example in MS research.
- 9
- This is particularly true when group sizes are small. This is in fact the case in experimental stroke research....
- let me explain
- Would this have any practical consequence? How different are these designs?
- Kant was perhaps the first to realize that the objectivity of scientific statements is closely connected with the construction of theories — with the use of hypotheses and universal statements. Only when certain events recur in accordance with rules or regularities, as is the case with repeatable experiments, can our observations be tested — in principle — by anyone. We do not take even our own observations quite seriously, or accept them as scientific observations, until we have repeated and tested them. Only by such repetitions can we convince ourselves that we are not dealing with a mere isolated ‘coincidence’, but with events which, on account of their regularity and reproducibility, are in principle inter-subjectively testable. Every experimental physicist knows those surprising and inexplicable apparent ‘effects’ which in his laboratory can perhaps even be reproduced for some time, but which finally disappear without trace. Of course, no physicist would say in such a case that he had made a scientific discovery (though he might try to rearrange his experiments so as to make the effect reproducible). Indeed the scientifically significant physical effect may be defined as that which can be regularly reproduced by anyone who carries out the appropriate experiment in the way prescribed. No serious physicist would offer for publication, as a scientific discovery, any such ‘occult effect,’ as I propose to call it — one for whose reproduction he could give no instructions. The ‘discovery’ would be only too soon rejected as chimerical, simply because attempts to test it would lead to negative results. (It follows that any controversy over the question whether events which are in principle unrepeatable and unique ever do occur cannot be decided by science: it would be a metaphysical controversy.) – Karl Popper (1959/2002), The Logic of Scientific Discovery, pp. 23-24.
- • Recent hand-wringing over failed replications in social psychology is largely pointless, because unsuccessful experiments have no meaningful scientific value. • Because experiments can be undermined by a vast number of practical mistakes, the likeliest explanation for any failed replication will always be that the replicator bungled something along the way. Unless direct replications are conducted by flawless experimenters, nothing interesting can be learned from them. • Three standard rejoinders to this critique are considered and rejected. Despite claims to the contrary, failed replications do not provide meaningful information if they closely follow original methodology; they do not necessarily identify effects that may be too small or flimsy to be worth studying; and they cannot contribute to a cumulative understanding of scientific phenomena. • Replication efforts appear to reflect strong prior expectations that published findings are not reliable, and as such, do not constitute scientific output. • The field of social psychology can be improved, but not by the publication of negative findings. Experimenters should be encouraged to restrict their “degrees of freedom,” for example, by specifying designs in advance. • Whether they mean to or not, authors and editors of failed replications are publicly impugning the scientific integrity of their colleagues. Targets of failed replications are justifiably upset, particularly given the inadequate basis for replicators’ extraordinary claims.
- power irrelevant, as experiment reproduced under identical conditions
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- The Amazing American Story of the Original Siamese Twins Few newcomers to the U.S. have crossed more daunting barriers than Chang and Eng Bunker