Why Is Peer Review of Research So Important in Establishing Scientific Theories Ap

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Why Virtually Published Enquiry Findings Are False

• John P. A. Ioannidis

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• Published: August 30, 2005
• https://doi.org/10.1371/periodical.pmed.0020124

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Abstract

Summary

There is increasing concern that near current published research findings are false. The probability that a inquiry claim is true may depend on study power and bias, the number of other studies on the same question, and, chiefly, the ratio of true to no relationships amongst the relationships probed in each scientific field. In this framework, a research finding is less likely to be true when the studies conducted in a field are smaller; when issue sizes are smaller; when at that place is a greater number and lesser preselection of tested relationships; where there is greater flexibility in designs, definitions, outcomes, and analytical modes; when in that location is greater fiscal and other interest and prejudice; and when more than teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more probable for a research claim to be imitation than true. Moreover, for many electric current scientific fields, claimed enquiry findings may often be simply authentic measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.

Citation: Ioannidis JPA (2005) Why Near Published Research Findings Are Fake. PLoS Med 2(8): e124. https://doi.org/10.1371/journal.pmed.0020124

Published: Baronial 30, 2005

Copyright: © 2005 John P. A. Ioannidis. This is an open-access article distributed under the terms of the Artistic Eatables Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Competing interests: The author has declared that no competing interests exist.

Abbreviation: PPV, positive predictive value

Published inquiry findings are sometimes refuted past subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen beyond the range of research designs, from clinical trials and traditional epidemiological studies [1–3] to the most modern molecular research [4,5]. At that place is increasing concern that in modern research, faux findings may be the majority or fifty-fifty the vast majority of published research claims [half dozen–8]. Yet, this should not be surprising. It can be proven that nearly claimed research findings are false. Here I will examine the fundamental factors that influence this trouble and some corollaries thereof.

Modeling the Framework for False Positive Findings

Several methodologists accept pointed out [9–11] that the high rate of nonreplication (lack of confirmation) of research discoveries is a result of the convenient, yet ill-founded strategy of claiming conclusive enquiry findings solely on the basis of a single report assessed past formal statistical significance, typically for a p-value less than 0.05. Research is not most appropriately represented and summarized by p-values, just, unfortunately, in that location is a widespread notion that medical inquiry articles should exist interpreted based only on p-values. Research findings are divers here as whatever relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, risk factors, or associations. "Negative" enquiry is besides very useful. "Negative" is actually a misnomer, and the misinterpretation is widespread. However, hither we will target relationships that investigators claim exist, rather than nil findings.

Information technology can be proven that most claimed research findings are false

As has been shown previously, the probability that a enquiry finding is indeed true depends on the prior probability of it existence true (earlier doing the study), the statistical power of the study, and the level of statistical significance [x,eleven]. Consider a 2 × 2 tabular array in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both truthful and false hypotheses can be made about the presence of relationships. Let R be the ratio of the number of "true relationships" to "no relationships" among those tested in the field. R is feature of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for but one or a few truthful relationships amongst thousands and millions of hypotheses that may be postulated. Let us as well consider, for computational simplicity, confining fields where either at that place is but one truthful human relationship (amid many that can be hypothesized) or the power is similar to find whatsoever of the several existing true relationships. The pre-report probability of a relationship being true is R/(R + ane). The probability of a study finding a true relationship reflects the power 1 - β (one minus the Type Two error rate). The probability of challenge a human relationship when none truly exists reflects the Type I error rate, α. Assuming that c relationships are existence probed in the field, the expected values of the two × 2 table are given in Table one. After a research finding has been claimed based on achieving formal statistical significance, the mail service-study probability that information technology is truthful is the positive predictive value, PPV. The PPV is likewise the complementary probability of what Wacholder et al. accept called the false positive study probability [10]. According to the 2 × 2 table, one gets PPV = (1 - β)R/(R - βR + α). A enquiry finding is thus more likely true than false if (1 - β)R > α. Since normally the vast majority of investigators depend on a = 0.05, this means that a research finding is more than probable truthful than simulated if (1 - β)R > 0.05.

What is less well appreciated is that bias and the extent of repeated independent testing by unlike teams of investigators around the globe may further misconstrue this picture and may lead to fifty-fifty smaller probabilities of the enquiry findings beingness indeed truthful. Nosotros will try to model these 2 factors in the context of similar ii × 2 tables.

Bias

Kickoff, allow us define bias every bit the combination of diverse design, data, analysis, and presentation factors that tend to produce enquiry findings when they should not be produced. Let u be the proportion of probed analyses that would not take been "research findings," but nevertheless end upwardly presented and reported as such, because of bias. Bias should not be confused with hazard variability that causes some findings to be false by run a risk fifty-fifty though the written report design, information, assay, and presentation are perfect. Bias can entail manipulation in the analysis or reporting of findings. Selective or distorted reporting is a typical form of such bias. We may assume that u does not depend on whether a true human relationship exists or not. This is not an unreasonable supposition, since typically information technology is impossible to know which relationships are indeed true. In the presence of bias (Table 2), one gets PPV = ([one - β]R + uβR)/(R + α − βR + u − uα + uβR), and PPV decreases with increasing u, unless 1 − β ≤ α, i.east., 1 − β ≤ 0.05 for virtually situations. Thus, with increasing bias, the chances that a inquiry finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure ane. Conversely, truthful research findings may occasionally exist annulled considering of reverse bias. For example, with large measurement errors relationships are lost in noise [12], or investigators utilize data inefficiently or neglect to detect statistically pregnant relationships, or there may be conflicts of interest that tend to "bury" pregnant findings [13]. There is no good large-scale empirical testify on how frequently such reverse bias may occur across diverse research fields. However, it is probably off-white to say that opposite bias is not as common. Moreover measurement errors and inefficient use of data are probably becoming less frequent problems, since measurement fault has decreased with technological advances in the molecular era and investigators are condign increasingly sophisticated about their data. Regardless, opposite bias may be modeled in the aforementioned fashion every bit bias to a higher place. As well opposite bias should not exist dislocated with chance variability that may lead to missing a truthful relationship because of gamble.

Testing by Several Contained Teams

Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, information technology is practically the rule that several research teams, oft dozens of them, may probe the same or similar questions. Unfortunately, in some areas, the prevailing mentality until at present has been to focus on isolated discoveries by unmarried teams and interpret research experiments in isolation. An increasing number of questions have at to the lowest degree one study claiming a enquiry finding, and this receives unilateral attending. The probability that at least one study, amidst several done on the aforementioned question, claims a statistically significant research finding is piece of cake to estimate. For due north independent studies of equal power, the 2 × 2 table is shown in Tabular array 3: PPV = R(1 − β ⁿ )/(R + 1 − [one − α] ⁿ − Rβ ^{due north} ) (non because bias). With increasing number of independent studies, PPV tends to subtract, unless one - β < a, i.e., typically one − β < 0.05. This is shown for dissimilar levels of ability and for different pre-study odds in Figure two. For n studies of dissimilar power, the term β ^{due north} is replaced by the product of the terms β _i for i = i to northward, only inferences are like.

Corollaries

A practical example is shown in Box 1. Based on the higher up considerations, ane may deduce several interesting corollaries virtually the probability that a research finding is indeed true.

Box 1. An Example: Science at Depression Pre-Study Odds

Let us assume that a team of investigators performs a whole genome association study to test whether whatsoever of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the illness, information technology is reasonable to await that probably around 10 gene polymorphisms amidst those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify whatever of them. Then R = x/100,000 = 10⁻⁴, and the pre-study probability for any polymorphism to exist associated with schizophrenia is also R/(R + 1) = ten⁻⁴. Permit united states also suppose that the study has lx% ability to notice an association with an odds ratio of i.3 at α = 0.05. And so information technology tin exist estimated that if a statistically significant association is establish with the p-value barely crossing the 0.05 threshold, the postal service-report probability that this is truthful increases about 12-fold compared with the pre-study probability, but it is still only 12 × ten⁻⁴.

At present let the states suppose that the investigators manipulate their pattern, analyses, and reporting so as to brand more relationships cantankerous the p = 0.05 threshold fifty-fifty though this would not have been crossed with a perfectly adhered to pattern and analysis and with perfect comprehensive reporting of the results, strictly according to the original study plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available "data mining" packages really are proud of their ability to yield statistically pregnant results through data dredging. In the presence of bias with u = 0.10, the mail-study probability that a research finding is truthful is simply 4.4 × 10^−iv. Furthermore, even in the absence of whatsoever bias, when 10 independent research teams perform similar experiments effectually the world, if 1 of them finds a formally statistically pregnant association, the probability that the research finding is true is only one.v × 10^−four, hardly any college than the probability nosotros had before any of this all-encompassing enquiry was undertaken!

Corollary 1: The smaller the studies conducted in a scientific field, the less probable the enquiry findings are to be true. Small sample size means smaller power and, for all functions to a higher place, the PPV for a true research finding decreases equally power decreases towards 1 − β = 0.05. Thus, other factors being equal, research findings are more probable true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology (several g subjects randomized) [xiv] than in scientific fields with small studies, such as most research of molecular predictors (sample sizes 100-fold smaller) [xv].

Corollary 2: The smaller the upshot sizes in a scientific field, the less probable the inquiry findings are to be true. Ability is also related to the effect size. Thus research findings are more than likely truthful in scientific fields with large furnishings, such equally the impact of smoking on cancer or cardiovascular affliction (relative risks 3–20), than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.1–1.5) [seven]. Modern epidemiology is increasingly obliged to target smaller result sizes [16]. Consequently, the proportion of true research findings is expected to decrease. In the aforementioned line of thinking, if the true issue sizes are very small in a scientific field, this field is probable to be plagued by almost ubiquitous fake positive claims. For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would exist largely utopian endeavors.

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 exist true. As shown to a higher place, the mail-written report probability that a finding is true (PPV) depends a lot on the pre-study odds (R). Thus, research findings are more likely true in confirmatory designs, such as large phase 3 randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and creative given the wealth of the assembled and tested data, such as microarrays and other high-throughput discovery-oriented research [4,8,17], should have extremely low PPV.

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 exist true. Flexibility increases the potential for transforming what would exist "negative" results into "positive" results, i.east., bias, u. For several research designs, e.g., randomized controlled trials [18–twenty] or meta-analyses [21,22], there have been efforts to standardize their bear and reporting. Adherence to common standards is likely to increment the proportion of true findings. The same applies to outcomes. True findings may be more than common when outcomes are unequivocal and universally agreed (e.g., death) rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes) [23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank exam) [24] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (east.g., artificial intelligence methods) and only "best" results are reported. Regardless, even in the most stringent research designs, bias seems to be a major trouble. For case, there is strong evidence that selective effect reporting, with manipulation of the outcomes and analyses reported, is a common problem even for randomized trails [25]. Simply abolishing selective publication would not make this problem become abroad.

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. Conflicts of interest and prejudice may increment bias, u. Conflicts of interest are very common in biomedical research [26], and typically they are inadequately and sparsely reported [26,27]. Prejudice may not necessarily take fiscal roots. Scientists in a given field may exist prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, academy-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may also lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review procedure the appearance and broadcasting of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical testify on expert stance shows that it is extremely unreliable [28].

Corollary vi: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to exist true. This seemingly paradoxical corollary follows considering, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explicate why we occasionally run into major excitement followed rapidly by astringent disappointments in fields that draw broad attending. With many teams working on the aforementioned field and with massive experimental information being produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its most impressive "positive" results. "Negative" results may become attractive for dissemination only if some other team has found a "positive" clan on the same question. In that example, it may be bonny to refute a merits made in some prestigious journal. The term Proteus phenomenon has been coined to depict this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [29]. Empirical testify suggests that this sequence of extreme opposites is very common in molecular genetics [29].

These corollaries consider each factor separately, but these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be modest may be more than likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators take enough to written report and search further and thus refrain from information dredging and manipulation.

Most Research Findings Are False for Most Research Designs and for Virtually Fields

In the described framework, a PPV exceeding 50% is quite hard to get. Tabular array iv provides the results of simulations using the formulas developed for the influence of ability, ratio of truthful to non-true relationships, and bias, for diverse types of situations that may exist characteristic of specific written report designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-written report chance that the intervention is effective is eventually true almost 85% of the fourth dimension. A adequately similar performance is expected of a confirmatory meta-analysis of good-quality randomized trials: potential bias probably increases, just power and pre-examination chances are college compared to a single randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to "correct" the low power of single studies, is probably false if R ≤ 1:iii. Research findings from underpowered, early-phase clinical trials would be truthful nearly i in four times, or even less frequently if bias is present. Epidemiological studies of an exploratory nature perform fifty-fifty worse, especially when underpowered, but even well-powered epidemiological studies may have but a one in 5 chance being true, if R = i:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,000-fold (e.yard., thirty,000 genes tested, of which thirty may be the true culprits) [30,31], PPV for each claimed human relationship is extremely depression, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.

Claimed Research Findings May Often Be Simply Accurate Measures of the Prevailing Bias

As shown, the majority of modern biomedical research is operating in areas with very low pre- and post-report probability for true findings. Let u.s.a. suppose that in a enquiry field in that location are no true findings at all to be discovered. History of science teaches united states of america that scientific attempt has often in the past wasted attempt in fields with admittedly no yield of true scientific information, at least based on our current understanding. In such a "null field," i would ideally expect all observed outcome sizes to vary past chance around the goose egg in the absenteeism of bias. The extent that observed findings deviate from what is expected past risk alone would be simply a pure mensurate of the prevailing bias.

For example, let united states suppose that no nutrients or dietary patterns are actually of import determinants for the risk of developing a specific tumor. Let united states of america too suppose that the scientific literature has examined 60 nutrients and claims all of them to be related to the risk of developing this tumor with relative risks in the range of 1.2 to ane.4 for the comparison of the upper to lower intake tertiles. And then the claimed effect sizes are simply measuring nothing else just the cyberspace bias that has been involved in the generation of this scientific literature. Claimed effect sizes are in fact the most accurate estimates of the net bias. It even follows that between "zilch fields," the fields that merits stronger effects (often with accompanying claims of medical or public health importance) are simply those that have sustained the worst biases.

For fields with very depression PPV, the few truthful relationships would not distort this overall moving-picture show much. Fifty-fifty if a few relationships are truthful, the shape of the distribution of the observed effects would all the same yield a clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators take viewed large and highly meaning effects with excitement, as signs of of import discoveries. Too big and too highly significant effects may actually exist more likely to exist signs of big bias in virtually fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.

Of course, investigators working in any field are likely to resist accepting that the whole field in which they have spent their careers is a "nix field." Yet, other lines of evidence, or advances in technology and experimentation, may lead eventually to the dismantling of a scientific field. Obtaining measures of the cyberspace bias in ane field may also be useful for obtaining insight into what might be the range of bias operating in other fields where similar belittling methods, technologies, and conflicts may be operating.

How Can We Improve the State of affairs?

Is it unavoidable that most research findings are false, or can we amend the state of affairs? A major problem is that it is impossible to know with 100% certainty what the truth is in any inquiry question. In this regard, the pure "gold" standard is unattainable. However, at that place are several approaches to meliorate the mail service-study probability.

Amend powered evidence, e.g., large studies or low-bias meta-analyses, may aid, as it comes closer to the unknown "gold" standard. Notwithstanding, big studies may still have biases and these should be acknowledged and avoided. Moreover, large-scale evidence is impossible to obtain for all of the millions and trillions of enquiry questions posed in electric current research. Large-scale testify should exist targeted for research questions where the pre-study probability is already considerably high, and so that a meaning research finding will lead to a postal service-test probability that would exist considered quite definitive. Large-scale evidence is also particularly indicated when it can test major concepts rather than narrow, specific questions. A negative finding tin can then refute not only a specific proposed claim, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on bigoted criteria, such equally the marketing promotion of a specific drug, is largely wasted research. Moreover, ane should be cautious that extremely big studies may be more likely to find a formally statistical meaning departure for a trivial result that is not really meaningfully different from the nil [32–34].

2d, almost enquiry questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced enquiry standards and curtailing of prejudices may likewise assist. However, this may require a modify in scientific mentality that might exist difficult to achieve. In some enquiry designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [35]. Registration would pose a challenge for hypothesis-generating inquiry. Some kind of registration or networking of information collections or investigators inside fields may be more viable than registration of each and every hypothesis-generating experiment. Regardless, even if we do not see a nifty deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.

Finally, instead of chasing statistical significance, we should meliorate our understanding of the range of R values—the pre-study odds—where research efforts operate [10]. Earlier running an experiment, investigators should consider what they believe the chances are that they are testing a true rather than a non-true relationship. Speculated high R values may sometimes so be ascertained. Equally described above, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to see how ofttimes they are indeed confirmed. I suspect several established "classics" will neglect the test [36].

Nevertheless, nigh new discoveries will continue to stalk from hypothesis-generating research with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the study of a single study gives just a partial picture, without knowing how much testing has been done exterior the written report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [37], normally it is incommunicable to decipher how much data dredging past the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would non inform us nearly the pre-study odds. Thus, it is unavoidable that 1 should make guess assumptions on how many relationships are expected to be true amid those probed across the relevant research fields and research designs. The wider field may yield some guidance for estimating this probability for the isolated enquiry projection. Experiences from biases detected in other neighboring fields would also be useful to draw upon. Fifty-fifty though these assumptions would be considerably subjective, they would all the same exist very useful in interpreting research claims and putting them in context.

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