Which of the Following Are Least Likely to Be Confirmed During the Peer Review Process?

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Why Most Published Research Findings Are False

• John P. A. Ioannidis

10

• Published: Baronial xxx, 2005
• https://doi.org/10.1371/journal.pmed.0020124

Figures

Abstract

Summary

At that place is increasing concern that most electric current published research findings are false. The probability that a research claim is true may depend on report power 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 true when the studies conducted in a field are smaller; when result sizes are smaller; when in that location 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 there is greater financial and other involvement and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations bear witness that for about study designs and settings, it is more probable for a enquiry claim to be fake than true. Moreover, for many current scientific fields, claimed inquiry findings may often exist merely accurate 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 Most Published Inquiry Findings Are False. PLoS Med 2(8): e124. https://doi.org/ten.1371/journal.pmed.0020124

Published: August xxx, 2005

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

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

Abridgement: PPV, positive predictive value

Published research findings are sometimes refuted by subsequent testify, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [1–three] to the most mod molecular research [iv,5]. There is increasing business organization that in modern research, false findings may be the majority or even the vast bulk of published research claims [six–8]. All the same, this should non be surprising. It can be proven that about claimed research findings are false. Here I volition examine the fundamental factors that influence this trouble and some corollaries thereof.

Modeling the Framework for False Positive Findings

Several methodologists take pointed out [9–xi] that the high rate of nonreplication (lack of confirmation) of research discoveries is a consequence of the user-friendly, notwithstanding ill-founded strategy of claiming conclusive research findings solely on the basis of a single study assessed by formal statistical significance, typically for a p-value less than 0.05. Research is non most appropriately represented and summarized past p-values, just, unfortunately, there is a widespread notion that medical research articles should exist interpreted based only on p-values. Enquiry findings are divers hither every bit any relationship reaching formal statistical significance, e.g., constructive interventions, informative predictors, chance factors, or associations. "Negative" research is besides very useful. "Negative" is actually a misnomer, and the misinterpretation is widespread. However, here nosotros will target relationships that investigators merits be, rather than null findings.

It tin exist proven that most claimed inquiry findings are imitation

Every bit has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it beingness true (before doing the written report), the statistical power of the study, and the level of statistical significance [10,11]. Consider a two × two table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both truthful and simulated hypotheses can be fabricated almost the presence of relationships. Let R exist the ratio of the number of "true relationships" to "no relationships" amongst 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 but one or a few true relationships amongst thousands and millions of hypotheses that may exist postulated. Allow u.s. besides consider, for computational simplicity, circumscribed fields where either there is only one truthful relationship (amongst many that can exist hypothesized) or the power is similar to find any of the several existing truthful relationships. The pre-study probability of a relationship beingness truthful is R/(R + ane). The probability of a report finding a truthful human relationship reflects the ability 1 - β (ane minus the Type II mistake rate). The probability of claiming a relationship when none truly exists reflects the Type I error charge per unit, α. Assuming that c relationships are being probed in the field, the expected values of the 2 × two table are given in Table 1. After a enquiry 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. have called the simulated positive report probability [10]. According to the 2 × 2 table, i gets PPV = (i - β)R/(R - βR + α). A research finding is thus more than likely true than false if (1 - β)R > α. Since usually the vast majority of investigators depend on a = 0.05, this means that a research finding is more likely true than false if (i - β)R > 0.05.

What is less well appreciated is that bias and the extent of repeated contained testing by different teams of investigators effectually the earth may further distort this movie and may lead to fifty-fifty smaller probabilities of the research findings being indeed true. We volition try to model these two factors in the context of like ii × 2 tables.

Bias

Outset, let u.s.a. define bias every bit the combination of various design, data, analysis, and presentation factors that tend to produce inquiry findings when they should non exist produced. Permit u be the proportion of probed analyses that would not have been "enquiry findings," but still end up presented and reported as such, considering of bias. Bias should not be confused with chance variability that causes some findings to be fake by gamble even though the study blueprint, data, analysis, and presentation are perfect. Bias tin can entail manipulation in the assay or reporting of findings. Selective or distorted reporting is a typical course of such bias. We may assume that u does non depend on whether a truthful relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed truthful. In the presence of bias (Table 2), one gets PPV = ([1 - β]R + uβR)/(R + α − βR + u − uα + uβR), and PPV decreases with increasing u, unless 1 − β ≤ α, i.e., ane − β ≤ 0.05 for most situations. Thus, with increasing bias, the chances that a enquiry finding is truthful diminish considerably. This is shown for different levels of power and for different pre-report odds in Figure 1. Conversely, true research findings may occasionally exist annulled considering of contrary bias. For example, with large measurement errors relationships are lost in noise [12], or investigators use information inefficiently or fail to notice statistically pregnant relationships, or there may be conflicts of involvement that tend to "coffin" significant findings [13]. In that location is no good large-calibration empirical evidence on how frequently such reverse bias may occur across various research fields. However, it is probably off-white to say that reverse bias is non equally mutual. Moreover measurement errors and inefficient use of data are probably condign less frequent problems, since measurement error has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated about their data. Regardless, reverse bias may be modeled in the same fashion as bias to a higher place. As well opposite bias should non exist confused with take a chance variability that may lead to missing a true relationship considering of hazard.

Testing by Several Independent Teams

Several independent teams may be addressing the same sets of enquiry 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 at present has been to focus on isolated discoveries by unmarried teams and interpret inquiry experiments in isolation. An increasing number of questions take at to the lowest degree one study claiming a research finding, and this receives unilateral attending. The probability that at least 1 study, among several done on the same question, claims a statistically pregnant research finding is piece of cake to estimate. For north contained studies of equal power, the two × 2 table is shown in Tabular array 3: PPV = R(1 − β ^{due north} )/(R + ane − [1 − α] ^north − Rβ ⁿ ) (not because bias). With increasing number of independent studies, PPV tends to decrease, unless 1 - β < a, i.e., typically one − β < 0.05. This is shown for different levels of power and for unlike pre-study odds in Figure ii. For n studies of different power, the term β ⁿ is replaced past the product of the terms β _i for i = 1 to n, just inferences are similar.

Corollaries

A practical case is shown in Box one. Based on the above considerations, ane may deduce several interesting corollaries virtually the probability that a research finding is indeed true.

Box i. An Case: Science at Low Pre-Report Odds

Let us presume that a team of investigators performs a whole genome association study to examination whether any of 100,000 factor 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 around x gene polymorphisms amongst those tested would be truly associated with schizophrenia, with relatively similar odds ratios effectually 1.3 for the ten or then polymorphisms and with a fairly similar ability to place any of them. So R = 10/100,000 = 10⁻⁴, and the pre-report probability for whatever polymorphism to be associated with schizophrenia is likewise R/(R + one) = 10^−four. Let the states besides suppose that the study has sixty% power to find an association with an odds ratio of 1.iii at α = 0.05. Then it can be estimated that if a statistically significant association is establish with the p-value barely crossing the 0.05 threshold, the post-written report probability that this is true increases about 12-fold compared with the pre-study probability, but it is still only 12 × 10⁻⁴.

Now let usa suppose that the investigators dispense their design, analyses, and reporting so every bit to make more relationships cross the p = 0.05 threshold even though this would not have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original study program. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, mail service hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the illness or command definitions, and various combinations of selective or distorted reporting of the results. Commercially available "data mining" packages actually are proud of their power to yield statistically significant results through data dredging. In the presence of bias with u = 0.10, the post-study probability that a research finding is true is simply 4.four × x⁻⁴. Furthermore, even in the absenteeism of any bias, when ten independent research teams perform similar experiments effectually the globe, if ane of them finds a formally statistically significant association, the probability that the enquiry finding is true is only 1.v × 10⁻⁴, inappreciably any higher than the probability we had before whatever of this extensive enquiry was undertaken!

Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be truthful. Small sample size means smaller ability and, for all functions above, the PPV for a true research finding decreases as power decreases towards i − β = 0.05. Thus, other factors being equal, research findings are more than probable true in scientific fields that undertake large studies, such every bit randomized controlled trials in cardiology (several thousand subjects randomized) [14] than in scientific fields with pocket-sized studies, such every bit most enquiry of molecular predictors (sample sizes 100-fold smaller) [15].

Corollary 2: The smaller the result sizes in a scientific field, the less likely the inquiry findings are to be truthful. Power is also related to the effect size. Thus inquiry findings are more probable truthful in scientific fields with large effects, such every bit the bear upon of smoking on cancer or cardiovascular affliction (relative risks three–twenty), than in scientific fields where postulated furnishings are modest, such every bit genetic risk factors for multigenetic diseases (relative risks 1.1–1.5) [7]. Modern epidemiology is increasingly obliged to target smaller effect sizes [16]. Consequently, the proportion of truthful research findings is expected to subtract. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to exist plagued by almost ubiquitous false positive claims. For instance, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than i.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 enquiry findings are to be true. Every bit shown above, the post-study probability that a finding is truthful (PPV) depends a lot on the pre-written report odds (R). Thus, research findings are more than likely true in confirmatory designs, such as big phase 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 data, such equally microarrays and other loftier-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 be true. Flexibility increases the potential for transforming what would exist "negative" results into "positive" results, i.e., bias, u. For several enquiry designs, e.1000., randomized controlled trials [xviii–20] or meta-analyses [21,22], there have been efforts to standardize their acquit 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 common when outcomes are unequivocal and universally agreed (e.g., decease) rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes) [23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (eastward.g., Kaplan-Meier plots and the log-rank exam) [24] may yield a larger proportion of true findings than fields where belittling methods are withal under experimentation (e.g., artificial intelligence methods) and only "best" results are reported. Regardless, even in the nigh stringent inquiry designs, bias seems to be a major problem. For example, there is strong evidence that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a common trouble even for randomized trails [25]. Simply abolishing selective publication would non make this trouble go abroad.

Corollary v: The greater the fiscal and other interests and prejudices in a scientific field, the less likely the research findings are to exist 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 fiscal 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 contained, 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 likewise pb to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review procedure the advent and dissemination of findings that refute their findings, thus condemning their field to perpetuate fake dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [28].

Corollary 6: The hotter a scientific field (with more than scientific teams involved), the less likely the enquiry findings are to be true. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the same field. This may explain why we occasionally come across major excitement followed apace past severe disappointments in fields that describe wide attending. With many teams working on the same field and with massive experimental information being produced, timing is of the essence in chirapsia contest. Thus, each squad may prioritize on pursuing and disseminating its almost impressive "positive" results. "Negative" results may become attractive for dissemination simply if some other team has plant a "positive" association on the same question. In that case, information technology may be attractive to refute a merits made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating farthermost research claims and extremely opposite refutations [29]. Empirical evidence suggests that this sequence of extreme opposites is very mutual in molecular genetics [29].

These corollaries consider each cistron separately, simply these factors often influence each other. For example, investigators working in fields where truthful effect sizes are perceived to be small may be more than likely to perform big 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 fifty-fifty create a bulwark 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 upshot in such a large yield of pregnant relationships that investigators have enough to report and search farther and thus refrain from data dredging and manipulation.

Most Research Findings Are False for Well-nigh Research Designs and for About Fields

In the described framework, a PPV exceeding l% is quite difficult to get. Table 4 provides the results of simulations using the formulas developed for the influence of power, ratio of truthful to non-true relationships, and bias, for various types of situations that may be feature of specific study designs and settings. A finding from a well-conducted, fairly powered randomized controlled trial starting with a 50% pre-study chance that the intervention is effective is eventually true about 85% of the time. A fairly similar performance is expected of a confirmatory meta-assay of adept-quality randomized trials: potential bias probably increases, but power and pre-test chances are college compared to a single randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to "correct" the depression ability of single studies, is probably false if R ≤ ane:three. Research findings from underpowered, early-phase clinical trials would be true most 1 in four times, or even less often if bias is nowadays. Epidemiological studies of an exploratory nature perform even worse, especially when underpowered, but even well-powered epidemiological studies may accept only a one in 5 take chances being true, if R = i:ten. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones i,000-fold (e.thou., 30,000 genes tested, of which xxx may be the true culprits) [30,31], PPV for each claimed relationship is extremely low, even with considerable standardization of laboratory and statistical methods, outcomes, and reporting thereof to minimize bias.

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

Every bit shown, the majority of modernistic biomedical research is operating in areas with very low pre- and postal service-study probability for truthful findings. Let u.s.a. suppose that in a research field there are no true findings at all to be discovered. History of science teaches us that scientific endeavor has often in the past wasted try in fields with absolutely no yield of true scientific data, at to the lowest degree based on our electric current understanding. In such a "null field," one would ideally wait all observed event sizes to vary by adventure around the zip in the absence of bias. The extent that observed findings deviate from what is expected by chance lone would be simply a pure measure of the prevailing bias.

For example, let us suppose that no nutrients or dietary patterns are really important determinants for the take chances of developing a specific tumor. Allow us also suppose that the scientific literature has examined sixty nutrients and claims all of them to exist related to the take chances of developing this tumor with relative risks in the range of 1.ii to ane.4 for the comparing of the upper to lower intake tertiles. So the claimed upshot sizes are simply measuring nothing else but the net bias that has been involved in the generation of this scientific literature. Claimed effect sizes are in fact the most accurate estimates of the internet bias. Information technology even follows that betwixt "zip fields," the fields that claim stronger effects (oftentimes with accompanying claims of medical or public wellness importance) are but those that have sustained the worst biases.

For fields with very low PPV, the few truthful relationships would not distort this overall picture much. Even if a few relationships are true, the shape of the distribution of the observed effects would still yield a articulate measure out of the biases involved in the field. This concept totally reverses the manner nosotros view scientific results. Traditionally, investigators take viewed large and highly significant effects with excitement, as signs of important discoveries. Also large and too highly meaning effects may really be more likely to exist signs of large bias in most fields of modern enquiry. They should lead investigators to conscientious critical thinking about what might have gone wrong with their information, 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 "cipher field." However, other lines of evidence, or advances in engineering and experimentation, may lead eventually to the dismantling of a scientific field. Obtaining measures of the net bias in one field may likewise 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 Tin Nosotros Improve the Situation?

Is it unavoidable that virtually research findings are simulated, or can nosotros ameliorate the situation? A major trouble is that it is incommunicable to know with 100% certainty what the truth is in any inquiry question. In this regard, the pure "gilt" standard is unattainable. Nonetheless, there are several approaches to improve the post-study probability.

Amend powered show, e.g., large studies or low-bias meta-analyses, may assist, as it comes closer to the unknown "gold" standard. However, large studies may however take biases and these should exist acknowledged and avoided. Moreover, large-calibration evidence is impossible to obtain for all of the millions and trillions of research questions posed in electric current enquiry. Large-scale evidence should be targeted for research questions where the pre-written report probability is already considerably high, then that a significant research finding will lead to a post-test probability that would be considered quite definitive. Large-scale evidence is also especially indicated when it tin can test major concepts rather than narrow, specific questions. A negative finding can then refute not simply a specific proposed claim, but a whole field or considerable portion thereof. Selecting the functioning of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. Moreover, one should be cautious that extremely big studies may be more probable to observe a formally statistical meaning difference for a petty effect that is non really meaningfully different from the goose egg [32–34].

Second, nigh research questions are addressed by many teams, and information technology is misleading to emphasize the statistically meaning findings of any single 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 crave a alter in scientific mentality that might exist difficult to achieve. In some research designs, efforts may also be more than successful with upfront registration of studies, eastward.g., randomized trials [35]. Registration would pose a claiming for hypothesis-generating research. Some kind of registration or networking of data collections or investigators within fields may be more feasible than registration of each and every hypothesis-generating experiment. Regardless, even if we do not encounter a smashing bargain 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 improve our understanding of the range of R values—the pre-written report odds—where research efforts operate [ten]. 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 high R values may sometimes then exist ascertained. As described above, whenever ethically acceptable, big studies with minimal bias should be performed on enquiry findings that are considered relatively established, to encounter how ofttimes they are indeed confirmed. I suspect several established "classics" will fail the exam [36].

Nevertheless, most new discoveries volition continue to stem from hypothesis-generating enquiry with low or very low pre-study odds. We should so acknowledge that statistical significance testing in the report of a single report gives but a partial film, without knowing how much testing has been washed exterior the written 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 not inform u.s.a. most the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true amongst those probed across the relevant research fields and inquiry 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 likewise be useful to draw upon. Fifty-fifty though these assumptions would be considerably subjective, they would still exist very useful in interpreting research claims and putting them in context.

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