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Ioannidis 2005

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Aurélien Madouasse
Aurélien Madouasse

The document summarizes John Ioannidis's article "Why Most Published Research Findings Are False." It outlines Ioannidis's modeling framework, which considers a population of hypotheses being tested, with some proportion being true. Hypothesis testing is framed as testing for a relationship or disease. The framework acknowledges the possibilities of making Type I (false positive) and Type II (false negative) errors. The document's central point is that if the proportion of true hypotheses is small and testing is noisy, most claimed research findings are likely to be false.

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Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Why most published research findings are false Article by John P. A. Ioannidis (2005) Aur´elien Madouasse November 4, 2011
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Plan 1 Context 2 Introduction 3 Modelling Framework Hypothesis testing Bias Multiple testing Comments 4 Corollaries 5 Conclusion
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Context • The author: John P.A. Ioannidis
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Context • The author: John P.A. Ioannidis • C.F. Rehnborg Chair in Disease Prevention at Stanford University (US) • Professor of Medicine and Director of the Stanford Prevention Research Center (US) • Chaired the Department of Hygiene and Epidemiology at the University of Ioannina School of Medicine (Greece) • Has a 51 page CV
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Context • The journal: PLoS Medicine
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Context • The journal: PLoS Medicine • Public Library of Science • Peer reviewed • Open Access • Publication fee: US$2900
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Context • The article (Checked 2011-10-22)
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Context • The article (Checked 2011-10-22) • Views: 410,087 • Citations: • CrossRef: 312 • PubMed Central: 118 • Scopus: 579 • Web of Science: 585
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Plan 1 Context 2 Introduction 3 Modelling Framework Hypothesis testing Bias Multiple testing Comments 4 Corollaries 5 Conclusion
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Introduction • Published research findings sometimes refuted by subsequent evidence • Increasing concern false findings may be the majority • This should no be surprising • Here is why . . .
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Plan 1 Context 2 Introduction 3 Modelling Framework Hypothesis testing Bias Multiple testing Comments 4 Corollaries 5 Conclusion
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis testing • Consider a parameter measured in a population of individuals with a disease: • Before treatment Some Parameter Frequency
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis testing • Consider a parameter measured in a population of individuals with a disease: • Before treatment • After treatment (Here assuming the treatment has an effect) Some Parameter Frequency
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect • We test our hypothesis
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect • We test our hypothesis • We get a result
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect • We test our hypothesis • We get a result • If H0 were true, the probability of observing our data would be . . .
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect • We test our hypothesis • We get a result • If H0 were true, the probability of observing our data would be . . . • p(data|H0) = p − value
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect • We test our hypothesis • We get a result • If H0 were true, the probability of observing our data would be . . . • p(data|H0) = p − value • We draw a conclusion
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect • We test our hypothesis • We get a result • If H0 were true, the probability of observing our data would be . . . • p(data|H0) = p − value • We draw a conclusion • If p(data|H0) > 0.05 we accept H0 → No effect
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • We want to know whether the treatment has an effect • We make a hypothesis • H0: The treatment has no effect • We test our hypothesis • We get a result • If H0 were true, the probability of observing our data would be . . . • p(data|H0) = p − value • We draw a conclusion • If p(data|H0) > 0.05 we accept H0 → No effect • If p(data|H0) ≤ 0.05 we reject H0 → Effect
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • This framework assumes that we accept to be wrong . . .
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • This framework assumes that we accept to be wrong . . . sometimes
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • This framework assumes that we accept to be wrong . . . sometimes Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • This framework assumes that we accept to be wrong . . . sometimes Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α • α = probability of declaring a relationship when there is none - Type I error
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • This framework assumes that we accept to be wrong . . . sometimes Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α • α = probability of declaring a relationship when there is none - Type I error • β = probability of finding no relationship when there is one - Type II error
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing • This framework assumes that we accept to be wrong . . . sometimes Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α • α = probability of declaring a relationship when there is none - Type I error • β = probability of finding no relationship when there is one - Type II error • 1 − β = probability of finding a relationship when there is one - Power
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Hypothesis Testing Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α • For a given hypothesis, whether we get it wrong depends on: • Whether the hypothesis is true • The magnitude of the effect • The values we choose for α and β Some Parameter Frequency Some Parameter Frequency Some Parameter Frequency
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Central point of the paper
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Central point of the paper • Consider a population of possible hypotheses
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Central point of the paper • Consider a population of possible hypotheses • Among these hypotheses, a proportion p are True
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Central point of the paper • Consider a population of possible hypotheses • Among these hypotheses, a proportion p are True • Hypothesis testing can be seen as testing for a disease in Epidemiology
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Central point of the paper • Consider a population of possible hypotheses • Among these hypotheses, a proportion p are True • Hypothesis testing can be seen as testing for a disease in Epidemiology • 1 − β is the sensitivity
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Central point of the paper • Consider a population of possible hypotheses • Among these hypotheses, a proportion p are True • Hypothesis testing can be seen as testing for a disease in Epidemiology • 1 − β is the sensitivity • 1 − α is the specificity
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Central point of the paper • Consider a population of possible hypotheses • Among these hypotheses, a proportion p are True • Hypothesis testing can be seen as testing for a disease in Epidemiology • 1 − β is the sensitivity • 1 − α is the specificity • We can define a positive predictive value
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Positive predictive value
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Positive predictive value PPV = p(1 − β) p(1 − β) + (1 − p)α
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationshipTrial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Positive predictive value • Ioannidis uses R = p 1−p PPV = R 1+R × (1 − β) R 1+R × (1 − β) + 1 1+R × α
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Modelling the Framework for False Positive Findings Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α Total p 1 − p • Positive predictive value • Ioannidis uses R = p 1−p PPV = R(1 − β) R(1 − β) + α
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias • Among the studies that should have been reported as negative Truth True relationship No relationship Trial Relationship 1 − β α No relationship β 1 − α Total p 1 − p
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias • Among the studies that should have been reported as negative • A proportion u are reported as positive because of bias Truth True relationship No relationship Trial Relationship 1 − β + uβ α + u(1 − α) No relationship (1 − u)β (1 − u)(1 − α) Total p 1 − p
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias Truth True relationship No relationship Trial Relationship 1 − β + uβ α + u(1 − α) No relationship (1 − u)β (1 − u)(1 − α) Total p 1 − p • Positive predictive value
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias Truth True relationship No relationship Trial Relationship 1 − β + uβ α + u(1 − α) No relationship (1 − u)β (1 − u)(1 − α) Total p 1 − p • Positive predictive value PPV = p(1 − β + uβ) p(1 − β + uβ) + (1 − p)(α + u(1 − α))
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias Truth True relationship No relationship Trial Relationship 1 − β + uβ α + u(1 − α) No relationship (1 − u)β (1 − u)(1 − α) Total p 1 − p • Positive predictive value • Ioannidis uses R = p 1−p PPV = R(1 − β) + uβR R + α − βR + u − uα + uβR
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) u = 0.05 u = 0.2 u = 0.5 u = 0.8 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) u = 0.05 u = 0.2 u = 0.5 u = 0.8 Power = 0.8
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) u = 0.05 u = 0.2 u = 0.5 u = 0.8 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) u = 0.05 u = 0.2 u = 0.5 u = 0.8 Power = 0.5
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Bias 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) u = 0.05 u = 0.2 u = 0.5 u = 0.8 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) u = 0.05 u = 0.2 u = 0.5 u = 0.8 Power = 0.2
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams • Increases the probability of a positive finding . . . by chance
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams • Increases the probability of a positive finding . . . by chance • Positive findings more likely to be published • Association with publication bias?
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams • Increases the probability of a positive finding . . . by chance • Positive findings more likely to be published • Association with publication bias? • Positive findings more likely to receive attention
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams • Increases the probability of a positive finding . . . by chance • Positive findings more likely to be published • Association with publication bias? • Positive findings more likely to receive attention • Probability of at least one positive finding:
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams • Increases the probability of a positive finding . . . by chance • Positive findings more likely to be published • Association with publication bias? • Positive findings more likely to receive attention • Probability of at least one positive finding: 1 - probability of negative findings only
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams • Increases the probability of a positive finding . . . by chance • Positive findings more likely to be published • Association with publication bias? • Positive findings more likely to receive attention • Probability of at least one positive finding: 1 - probability of negative findings only Truth True relationship No relationship Trial Relationship 1 − βn 1 − (1 − α)n No relationship βn (1 − α)n
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams Truth True relationship No relationship Trial Relationship 1 − βn 1 − (1 − α)n No relationship βn (1 − α)n Total p 1 − p • Positive predictive value
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams Truth True relationship No relationshipTrial Relationship 1 − βn 1 − (1 − α)n No relationship βn (1 − α)n Total p 1 − p • Positive predictive value PPV = p(1 − βn) p(1 − βn) + (1 − p)(1 − (1 − α)n)
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams Truth True relationship No relationshipTrial Relationship 1 − βn 1 − (1 − α)n No relationship βn (1 − α)n Total p 1 − p • Positive predictive value • Ioannidis uses R = p 1−p PPV = R(1 − βn) R + 1 − ((1 − α)n + Rβn)
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) n = 1 n = 5 n = 10 n = 50 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) n = 1 n = 5 n = 10 n = 50 Power = 0.8
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) n = 1 n = 5 n = 10 n = 50 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) n = 1 n = 5 n = 10 n = 50 Power = 0.5
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Testing by Several Independent Teams 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study odds Post−studyprobability(PPV) n = 1 n = 5 n = 10 n = 50 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 Pre−study probability Post−studyprobability(PPV) n = 1 n = 5 n = 10 n = 50 Power = 0.2
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow • Odds
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow • Odds • Max 1 on the plots i.e. p ≤ 0.5
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow • Odds • Max 1 on the plots i.e. p ≤ 0.5 • Plausible values?
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow • Odds • Max 1 on the plots i.e. p ≤ 0.5 • Plausible values? • It would be great if the framework could be formally assessed for various scientific fields!
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow • Odds • Max 1 on the plots i.e. p ≤ 0.5 • Plausible values? • It would be great if the framework could be formally assessed for various scientific fields! • Typical values for p and u in Veterinary Epidemiology???
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow • Odds • Max 1 on the plots i.e. p ≤ 0.5 • Plausible values? • It would be great if the framework could be formally assessed for various scientific fields! • Typical values for p and u in Veterinary Epidemiology??? • Is it possible to design a study to estimate these???
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The use of odds instead of probabilities makes the article hard to follow • Odds • Max 1 on the plots i.e. p ≤ 0.5 • Plausible values? • It would be great if the framework could be formally assessed for various scientific fields! • Typical values for p and u in Veterinary Epidemiology??? • Is it possible to design a study to estimate these??? • Problem: Gold Standard
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • Link between magnitude of the effect, α, β and sample size Some Parameter Frequency Some Parameter Frequency Some Parameter Frequency
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • Link between magnitude of the effect, α, β and sample size • Trade off between α and β Some Parameter Frequency Some Parameter Frequency Some Parameter Frequency
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • Link between magnitude of the effect, α, β and sample size • Trade off between α and β • Smaller effects require bigger samples Some Parameter Frequency Some Parameter Frequency Some Parameter Frequency
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Comments on the framework • The corollaries follow from the proposed model
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Plan 1 Context 2 Introduction 3 Modelling Framework Hypothesis testing Bias Multiple testing Comments 4 Corollaries 5 Conclusion
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Corollary 1 The smaller the studies conducted in a scientific field, the less likely the research findings are to be true
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Corollary 2 The smaller the effect sizes in a scientific field, the less likely the research findings are to be true
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Corollary 3 The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Corollary 4 The greater the flexibility in designs, definitions, outcomes and analytical modes in a scientific field, the less likely the research findings are to be true
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Corollary 5 The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Corollary 6 The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion Plan 1 Context 2 Introduction 3 Modelling Framework Hypothesis testing Bias Multiple testing Comments 4 Corollaries 5 Conclusion
Why most published research findings are false Aur´elien Madouasse Context Introduction Modelling Framework Hypothesis testing Bias Multiple testing Comments Corollaries Conclusion How can we improve the situation? • Cannot draw firm conclusions based on a single positive result • It is possible to test for something until we find what we want! • And this is more likely to receive attention • Selecting research questions • Avoid marketing driven questions • Importance of pre study odds • Increase power • Larger samples • For research questions with high pre-study odds • To test major concepts rather than narrow specific questions • Research standards

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Ioannidis 2005