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Why Most Published Research Findings Are False

• John P. A. Ioannidis

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• Published: August 30, 2005
• https://doi.org/ten.1371/periodical.pmed.0020124

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Abstract

Summary

At that place is increasing business that nearly electric current published research findings are false. The probability that a enquiry claim is truthful may depend on study ability and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships among the relationships probed in each scientific field. In this framework, a enquiry finding is less likely to be truthful when the studies conducted in a field are smaller; when effect sizes are smaller; when there is a greater number and lesser preselection of tested relationships; where in that location is greater flexibility in designs, definitions, outcomes, and analytical modes; when at that place is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations testify that for well-nigh study designs and settings, it is more than likely for a research merits to be faux than true. Moreover, for many current scientific fields, claimed research findings may ofttimes exist only accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the comport and estimation of research.

Citation: Ioannidis JPA (2005) Why Most Published Research Findings Are Fake. PLoS Med 2(eight): e124. https://doi.org/x.1371/journal.pmed.0020124

Published: August xxx, 2005

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

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

Abbreviation: PPV, positive predictive value

Published research findings are sometimes refuted by 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 [one–3] to the well-nigh modern molecular research [4,five]. At that place is increasing concern that in modernistic enquiry, false findings may be the majority or even the vast majority of published inquiry claims [6–8]. Withal, this should non be surprising. Information technology can be proven that nearly claimed research findings are simulated. Hither I volition examine the primal factors that influence this problem and some corollaries thereof.

Modeling the Framework for False Positive Findings

Several methodologists have pointed out [nine–11] that the high charge per unit of nonreplication (lack of confirmation) of research discoveries is a outcome of the user-friendly, yet ill-founded strategy of challenge conclusive enquiry findings solely on the footing of a single study assessed by formal statistical significance, typically for a p-value less than 0.05. Research is not most appropriately represented and summarized past p-values, just, unfortunately, there is a widespread notion that medical inquiry manufactures should be interpreted based simply on p-values. Enquiry findings are defined here as whatever relationship reaching formal statistical significance, e.g., effective interventions, informative predictors, take chances factors, or associations. "Negative" research is also very useful. "Negative" is actually a misnomer, and the misinterpretation is widespread. However, here we will target relationships that investigators claim exist, rather than null findings.

It can be proven that near claimed enquiry findings are faux

Every bit has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true (earlier doing the study), the statistical ability of the study, and the level of statistical significance [10,11]. Consider a 2 × 2 table in which research findings are compared against the aureate standard of truthful relationships in a scientific field. In a research field both true and fake hypotheses can be made about the presence of relationships. Allow R be the ratio of the number of "truthful relationships" to "no relationships" among those tested in the field. R is characteristic of the field and can vary a lot depending on whether the field targets highly likely relationships or searches for only one or a few truthful relationships amongst thousands and millions of hypotheses that may be postulated. Let u.s. also consider, for computational simplicity, circumscribed fields where either there is only ane true relationship (among many that can exist hypothesized) or the power is similar to discover any of the several existing truthful relationships. The pre-study probability of a human relationship existence true is R/(R + 1). The probability of a study finding a true relationship reflects the ability 1 - β (1 minus the Type II error rate). The probability of claiming a relationship when none truly exists reflects the Type I error rate, α. Assuming that c relationships are being probed in the field, the expected values of the ii × 2 table are given in Table 1. After a research finding has been claimed based on achieving formal statistical significance, the post-report probability that it is true is the positive predictive value, PPV. The PPV is also the complementary probability of what Wacholder et al. take called the false positive report probability [x]. According to the ii × 2 table, i gets PPV = (1 - β)R/(R - βR + α). A research finding is thus more likely true than fake if (1 - β)R > α. Since ordinarily the vast majority of investigators depend on a = 0.05, this means that a enquiry finding is more probable truthful than false if (i - β)R > 0.05.

What is less well appreciated is that bias and the extent of repeated independent testing past different teams of investigators around the globe may further misconstrue this flick and may lead to even smaller probabilities of the research findings being indeed true. We will endeavour to model these two factors in the context of similar 2 × 2 tables.

Bias

Outset, let u.s.a. define bias as the combination of various design, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Permit u be the proportion of probed analyses that would not accept been "research findings," but yet end upwardly presented and reported as such, because of bias. Bias should not be confused with chance variability that causes some findings to be faux by chance even though the study blueprint, data, 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 non depend on whether a truthful relationship exists or non. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true. In the presence of bias (Tabular array ii), i gets PPV = ([one - β]R + uβR)/(R + α − βR + u − uα + uβR), and PPV decreases with increasing u, unless 1 − β ≤ α, i.e., one − β ≤ 0.05 for nigh situations. Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for unlike pre-study odds in Effigy 1. 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 use data inefficiently or fail to notice statistically significant relationships, or in that location may be conflicts of interest that tend to "bury" significant findings [thirteen]. There is no good large-scale empirical testify on how frequently such reverse bias may occur across diverse inquiry fields. However, it is probably fair to say that reverse bias is not as common. Moreover measurement errors and inefficient use of data are probably condign less frequent problems, since measurement fault has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated nearly their data. Regardless, reverse bias may be modeled in the same way equally bias above. Too reverse bias should not be confused with chance variability that may lead to missing a truthful human relationship because of chance.

Testing by Several Contained Teams

Several independent teams may be addressing the same sets of research questions. As research efforts are globalized, it is practically the rule that several research teams, often dozens of them, may probe the aforementioned or similar questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret inquiry experiments in isolation. An increasing number of questions have at to the lowest degree i study challenge a research finding, and this receives unilateral attention. The probability that at to the lowest degree ane study, amongst several done on the same question, claims a statistically meaning inquiry finding is easy to estimate. For north independent studies of equal ability, the ii × 2 tabular array is shown in Tabular array 3: PPV = R(1 − β ⁿ )/(R + i − [1 − α] ^north − Rβ ^{due north} ) (not considering bias). With increasing number of independent studies, PPV tends to decrease, unless ane - β < a, i.e., typically i − β < 0.05. This is shown for dissimilar levels of ability and for dissimilar pre-report odds in Figure 2. For n studies of different power, the term β ^north is replaced past the product of the terms β _i for i = i to n, simply inferences are similar.

Corollaries

A applied example is shown in Box 1. Based on the above considerations, 1 may deduce several interesting corollaries nigh the probability that a research finding is indeed true.

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

Let us presume that a team of investigators performs a whole genome association written report to test whether any of 100,000 gene polymorphisms are associated with susceptibility to schizophrenia. Based on what we know about the extent of heritability of the disease, it is reasonable to expect that probably effectually ten gene polymorphisms among those tested would exist truly associated with schizophrenia, with relatively similar odds ratios effectually 1.3 for the x or so polymorphisms and with a fairly similar power to place any of them. Then R = ten/100,000 = 10⁻⁴, and the pre-study probability for any polymorphism to be associated with schizophrenia is likewise R/(R + 1) = x⁻⁴. Let u.s. also suppose that the report has threescore% power to find an association with an odds ratio of 1.3 at α = 0.05. And so information technology can be estimated that if a statistically significant association is found with the p-value barely crossing the 0.05 threshold, the mail service-written report probability that this is true increases nearly 12-fold compared with the pre-report probability, but it is notwithstanding only 12 × 10^−four.

Now allow the states suppose that the investigators manipulate their pattern, analyses, and reporting then every bit to make more than relationships cantankerous the p = 0.05 threshold even though this would non have been crossed with a perfectly adhered to blueprint and assay and with perfect comprehensive reporting of the results, strictly according to the original study programme. Such manipulation could be done, for case, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were non originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially bachelor "data mining" packages actually are proud of their power to yield statistically significant results through information dredging. In the presence of bias with u = 0.10, the post-report probability that a enquiry finding is true is only 4.four × ten⁻⁴. Furthermore, even in the absenteeism of any bias, when ten independent enquiry teams perform like experiments around the world, if one of them finds a formally statistically significant clan, the probability that the research finding is true is only 1.5 × 10^−iv, inappreciably whatsoever higher than the probability we had before whatsoever of this all-encompassing research was undertaken!

Corollary 1: The smaller the studies conducted in a scientific field, the less probable the research findings are to be true. Minor sample size means smaller ability and, for all functions in a higher place, the PPV for a true research finding decreases every bit power decreases towards 1 − β = 0.05. Thus, other factors existence equal, research findings are more likely true in scientific fields that undertake big studies, such every bit randomized controlled trials in cardiology (several one thousand subjects randomized) [fourteen] than in scientific fields with modest studies, such every bit nigh research of molecular predictors (sample sizes 100-fold smaller) [fifteen].

Corollary ii: The smaller the effect sizes in a scientific field, the less likely the enquiry findings are to be truthful. Power is too related to the effect size. Thus research findings are more likely true in scientific fields with large furnishings, such as the touch on of smoking on cancer or cardiovascular disease (relative risks 3–20), than in scientific fields where postulated furnishings are small, such every bit genetic risk factors for multigenetic diseases (relative risks ane.1–1.5) [7]. Mod epidemiology is increasingly obliged to target smaller effect sizes [xvi]. Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the truthful result sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous fake positive claims. For example, if the bulk of true genetic or nutritional determinants of circuitous diseases confer relative risks less than 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.

Corollary 3: The greater the number and the bottom the selection of tested relationships in a scientific field, the less probable the research findings are to be truthful. As shown higher up, the post-study probability that a finding is truthful (PPV) depends a lot on the pre-study odds (R). Thus, inquiry findings are more likely true in confirmatory designs, such as large stage III randomized controlled trials, or meta-analyses thereof, than in hypothesis-generating experiments. Fields considered highly informative and artistic given the wealth of the assembled and tested information, such as microarrays and other loftier-throughput discovery-oriented research [4,8,17], should take extremely low PPV.

Corollary 4: The greater the flexibility in designs, definitions, outcomes, and analytical modes in a scientific field, the less probable the inquiry findings are to be truthful. Flexibility increases the potential for transforming what would be "negative" results into "positive" results, i.due east., bias, u. For several research designs, e.yard., randomized controlled trials [xviii–xx] or meta-analyses [21,22], at that place take been efforts to standardize their conduct and reporting. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may exist more common when outcomes are unequivocal and universally agreed (due east.one thousand., expiry) rather than when multifarious outcomes are devised (e.1000., scales for schizophrenia outcomes) [23]. Similarly, fields that use usually agreed, stereotyped analytical methods (east.thousand., Kaplan-Meier plots and the log-rank test) [24] may yield a larger proportion of truthful findings than fields where belittling methods are still under experimentation (e.m., artificial intelligence methods) and only "all-time" results are reported. Regardless, even in the most stringent inquiry designs, bias seems to be a major problem. For example, in that location is stiff evidence that selective outcome 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 trouble go away.

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 increase 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 have financial roots. Scientists in a given field may be prejudiced purely because of their belief in a scientific theory or commitment to their own findings. Many otherwise seemingly independent, university-based studies may be conducted for no other reason than to give physicians and researchers qualifications for promotion or tenure. Such nonfinancial conflicts may too lead to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review procedure the appearance and dissemination of findings that refute their findings, thus condemning their field to perpetuate fake dogma. Empirical bear witness on expert stance shows that information technology is extremely unreliable [28].

Corollary 6: The hotter a scientific field (with more than scientific teams involved), the less probable the research findings are to be true. This seemingly paradoxical corollary follows because, equally stated in a higher place, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally see major excitement followed quickly past astringent disappointments in fields that describe wide attending. With many teams working on the same field and with massive experimental data beingness produced, timing is of the essence in beating competition. Thus, each team may prioritize on pursuing and disseminating its about impressive "positive" results. "Negative" results may become bonny for dissemination only if some other squad has found a "positive" clan on the same question. In that case, it may exist bonny to abnegate 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 evidence 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 truthful result sizes are perceived to be pocket-size 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, farther 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 meaning relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.

Almost Inquiry Findings Are False for Nigh Research Designs and for Most Fields

In the described framework, a PPV exceeding fifty% is quite difficult to become. Tabular array 4 provides the results of simulations using the formulas developed for the influence of power, ratio of true to non-true relationships, and bias, for various types of situations that may exist characteristic of specific study designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a l% pre-written report chance that the intervention is constructive is eventually true virtually 85% of the time. A fairly similar performance is expected of a confirmatory meta-assay of good-quality randomized trials: potential bias probably increases, merely power and pre-test chances are higher 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 ≤ 1:3. Research findings from underpowered, early-phase clinical trials would exist true about ane in four times, or even less often if bias is present. Epidemiological studies of an exploratory nature perform fifty-fifty worse, especially when underpowered, but even well-powered epidemiological studies may accept merely a i in five chance being true, if R = 1:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones 1,000-fold (e.k., 30,000 genes tested, of which 30 may be the true culprits) [xxx,31], PPV for each claimed human relationship is extremely low, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.

Claimed Enquiry 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 postal service-written report probability for true findings. Allow us suppose that in a research field there are no true findings at all to exist discovered. History of science teaches us that scientific attempt has oft in the by wasted effort in fields with admittedly no yield of true scientific information, at least based on our electric current understanding. In such a "null field," ane would ideally expect all observed result sizes to vary past chance around the aught in the absence of bias. The extent that observed findings deviate from what is expected by risk alone would be simply a pure measure of the prevailing bias.

For example, let united states of america suppose that no nutrients or dietary patterns are actually important determinants for the gamble of developing a specific tumor. Let united states besides suppose that the scientific literature has examined 60 nutrients and claims all of them to be related to the run a risk of developing this tumor with relative risks in the range of ane.2 to 1.4 for the comparison of the upper to lower intake tertiles. Then the claimed effect sizes are merely measuring cypher else but the internet 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 "null fields," the fields that claim 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 low PPV, the few truthful relationships would non misconstrue this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would even so yield a clear mensurate of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators accept viewed large and highly pregnant effects with excitement, equally signs of important discoveries. Besides large and too highly significant effects may actually be more likely to exist signs of large bias in virtually fields of modern inquiry. They should lead investigators to conscientious disquisitional thinking about what might take gone wrong with their data, analyses, and results.

Of form, investigators working in any field are probable to resist accepting that the whole field in which they have spent their careers is a "null field." Still, other lines of evidence, or advances in applied science and experimentation, may atomic number 82 eventually to the dismantling of a scientific field. Obtaining measures of the net bias in one field may also be useful for obtaining insight into what might be the range of bias operating in other fields where similar analytical methods, technologies, and conflicts may be operating.

How Can We Amend the Situation?

Is it unavoidable that nigh research findings are false, or can nosotros improve the situation? A major problem is that it is incommunicable to know with 100% certainty what the truth is in whatsoever enquiry question. In this regard, the pure "gilt" standard is unattainable. However, there are several approaches to improve the post-report probability.

Improve powered evidence, east.k., large studies or depression-bias meta-analyses, may assist, every bit it comes closer to the unknown "gold" standard. However, large studies may still have biases and these should exist acknowledged and avoided. Moreover, big-scale evidence is impossible to obtain for all of the millions and trillions of research questions posed in current research. Big-calibration testify should be targeted for research questions where the pre-study probability is already considerably high, and so that a significant inquiry finding will lead to a post-test probability that would be considered quite definitive. Large-calibration evidence is also particularly indicated when information technology tin test major concepts rather than narrow, specific questions. A negative finding tin can so abnegate not merely a specific proposed merits, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. Moreover, ane should be cautious that extremely big studies may be more probable to find a formally statistical significant departure for a trivial effect that is non actually meaningfully different from the null [32–34].

Second, most research questions are addressed past many teams, and it is misleading to emphasize the statistically pregnant findings of whatever unmarried team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to reach. In some research designs, efforts may also exist 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 data collections or investigators within fields may exist more feasible than registration of each and every hypothesis-generating experiment. Regardless, fifty-fifty if we practise not see a great bargain of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more than widely borrowed from randomized controlled trials.

Finally, instead of chasing statistical significance, we should better our understanding of the range of R values—the pre-study odds—where research efforts operate [10]. Before 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 loftier R values may sometimes and so be ascertained. As described higher up, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to run across how ofttimes they are indeed confirmed. I suspect several established "classics" will fail the examination [36].

Yet, well-nigh new discoveries will keep to stem from hypothesis-generating inquiry with low or very low pre-study odds. We should then acknowledge that statistical significance testing in the study of a single study gives simply a partial flick, without knowing how much testing has been done outside the report and in the relevant field at large. Despite a large statistical literature for multiple testing corrections [37], usually it is impossible to decipher how much data dredging by the reporting authors or other research teams has preceded a reported research finding. Even if determining this were feasible, this would non inform us about the pre-report odds. Thus, it is unavoidable that one should brand approximate assumptions on how many relationships are expected to be truthful among those probed across the relevant research fields and enquiry designs. The wider field may yield some guidance for estimating this probability for the isolated inquiry project. Experiences from biases detected in other neighboring fields would also be useful to depict upon. Fifty-fifty though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context.

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Source: https://journals.plos.org/plosmedicine/article?id=10.1371%2Fjournal.pmed.0020124

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