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

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Why Almost Published Research Findings Are False

• John P. A. Ioannidis

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• Published: Baronial 30, 2005
• https://doi.org/x.1371/journal.pmed.0020124

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Abstruse

Summary

There is increasing concern that nearly current published research findings are false. The probability that a inquiry merits is true may depend on written report power and bias, the number of other studies on the same question, and, importantly, the ratio of true to no relationships amid the relationships probed in each scientific field. In this framework, a research finding is less likely to exist true 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 there is greater flexibility in designs, definitions, outcomes, and analytical modes; when in that location is greater financial and other interest and prejudice; and when more than teams are involved in a scientific field in hunt of statistical significance. Simulations bear witness that for most report designs and settings, information technology is more likely for a research claim to be fake than truthful. Moreover, for many current scientific fields, claimed enquiry findings may often exist simply accurate measures of the prevailing bias. In this essay, I talk over 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(viii): e124. https://doi.org/10.1371/journal.pmed.0020124

Published: Baronial xxx, 2005

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

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

Abbreviation: PPV, positive predictive value

Published enquiry findings are sometimes refuted by subsequent evidence, with ensuing defoliation and thwarting. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [i–3] to the most modern molecular research [4,5]. There is increasing business organisation that in modern research, imitation findings may exist the majority or fifty-fifty the vast majority of published research claims [6–8]. However, this should not be surprising. It can be proven that well-nigh claimed research findings are false. Here I will examine the primal factors that influence this problem and some corollaries thereof.

Modeling the Framework for False Positive Findings

Several methodologists accept pointed out [9–11] that the loftier rate of nonreplication (lack of confirmation) of enquiry discoveries is a consequence of the convenient, all the same ill-founded strategy of claiming conclusive inquiry findings solely on the basis of a single report assessed by formal statistical significance, typically for a p-value less than 0.05. Inquiry is not well-nigh appropriately represented and summarized by p-values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based just on p-values. Research findings are defined hither as whatsoever human relationship reaching formal statistical significance, eastward.thousand., effective interventions, informative predictors, risk 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 merits be, rather than null findings.

It can be proven that most claimed research findings are false

Equally has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it existence true (before doing the study), the statistical power of the written report, and the level of statistical significance [x,11]. Consider a 2 × 2 table in which research findings are compared against the gold standard of true relationships in a scientific field. In a research field both true and false hypotheses can be made almost 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 characteristic of the field and can vary a lot depending on whether the field targets highly probable relationships or searches for only one or a few true relationships among thousands and millions of hypotheses that may be postulated. Let the states too consider, for computational simplicity, circumscribed fields where either at that place is only ane true relationship (amid 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 human relationship being true is R/(R + one). The probability of a report finding a truthful relationship reflects the power one - β (1 minus the Type II mistake rate). The probability of claiming a relationship when none truly exists reflects the Blazon I fault charge per unit, α. Assuming that c relationships are being probed in the field, the expected values of the 2 × 2 table are given in Table 1. Afterward a research finding has been claimed based on achieving formal statistical significance, the post-study 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 faux positive report probability [10]. According to the 2 × 2 table, one gets PPV = (1 - β)R/(R - βR + α). A research finding is thus more probable true than imitation if (1 - β)R > α. Since usually the vast majority of investigators depend on a = 0.05, this ways that a research finding is more than likely true than simulated if (i - β)R > 0.05.

What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the earth may further misconstrue this motion-picture show and may lead to fifty-fifty smaller probabilities of the research findings being indeed true. We will attempt to model these two factors in the context of similar 2 × ii tables.

Bias

Kickoff, permit us ascertain bias as the combination of various blueprint, data, analysis, and presentation factors that tend to produce research findings when they should not be produced. Permit u exist the proportion of probed analyses that would not have been "research findings," but nevertheless end up presented and reported every bit such, because of bias. Bias should not exist confused with take chances variability that causes some findings to be simulated by hazard even though the study design, data, analysis, and presentation are perfect. Bias can entail manipulation in the assay 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 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 two), one gets PPV = ([1 - β]R + uβR)/(R + α − βR + u − uα + uβR), and PPV decreases with increasing u, unless ane − β ≤ α, i.due east., 1 − β ≤ 0.05 for nigh 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, true research findings may occasionally exist annulled considering of reverse bias. For example, with large measurement errors relationships are lost in racket [12], or investigators use data inefficiently or fail to notice statistically significant relationships, or at that place may be conflicts of interest that tend to "bury" meaning findings [13]. There is no good big-calibration empirical evidence on how frequently such opposite bias may occur across diverse inquiry fields. Nevertheless, it is probably fair to say that reverse bias is not every bit common. Moreover measurement errors and inefficient apply of information are probably becoming less frequent problems, since measurement mistake has decreased with technological advances in the molecular era and investigators are becoming increasingly sophisticated nigh their data. Regardless, contrary bias may be modeled in the aforementioned way as bias above. Also contrary bias should not be confused with chance variability that may atomic number 82 to missing a truthful relationship because of gamble.

Testing by Several Independent Teams

Several independent teams may exist addressing the same sets of research questions. As inquiry efforts are globalized, it is practically the rule that several inquiry teams, often dozens of them, may probe the same or like questions. Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by unmarried teams and interpret research experiments in isolation. An increasing number of questions have at least 1 study challenge a enquiry finding, and this receives unilateral attention. The probability that at to the lowest degree i written report, among several done on the same question, claims a statistically significant research finding is like shooting fish in a barrel to estimate. For n independent studies of equal power, the 2 × 2 table is shown in Table 3: PPV = R(ane − β ^north )/(R + ane − [one − α] ⁿ − Rβ ⁿ ) (not because bias). With increasing number of independent studies, PPV tends to subtract, unless 1 - β < a, i.due east., typically 1 − β < 0.05. This is shown for different levels of power and for different pre-study odds in Figure 2. For n studies of different power, the term β ⁿ is replaced by the production of the terms β _i for i = i to n, only inferences are similar.

Corollaries

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

Box ane. An Example: Scientific discipline at Low Pre-Study Odds

Let us assume 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 nosotros know about the extent of heritability of the disease, information technology is reasonable to expect that probably around ten cistron polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.three for the ten or and so polymorphisms and with a adequately similar power to place whatsoever of them. And then R = 10/100,000 = ten^−iv, and the pre-study probability for whatsoever polymorphism to be associated with schizophrenia is also R/(R + 1) = 10⁻⁴. Permit united states also suppose that the study has threescore% power to find an association with an odds ratio of ane.3 at α = 0.05. And so information technology tin can exist 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 information technology is withal only 12 × 10⁻⁴.

Now allow u.s. suppose that the investigators manipulate their design, analyses, and reporting so as to make more than relationships cross the p = 0.05 threshold even though this would non have been crossed with a perfectly adhered to design and analysis and with perfect comprehensive reporting of the results, strictly according to the original written report programme. 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 information dredging. In the presence of bias with u = 0.ten, the postal service-report probability that a research finding is true is only 4.4 × 10⁻⁴. Furthermore, even in the absence of any bias, when x contained research teams perform similar experiments around the world, if i of them finds a formally statistically significant association, the probability that the research finding is true is just one.5 × ten⁻⁴, hardly whatever higher than the probability we had earlier whatever of this extensive research was undertaken!

Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Pocket-size sample size means smaller ability and, for all functions above, the PPV for a true research finding decreases every bit ability decreases towards 1 − β = 0.05. Thus, other factors existence equal, inquiry findings are more than probable true in scientific fields that undertake big studies, such as randomized controlled trials in cardiology (several chiliad subjects randomized) [xiv] than in scientific fields with small studies, such equally most research of molecular predictors (sample sizes 100-fold smaller) [15].

Corollary 2: The smaller the issue sizes in a scientific field, the less likely the research findings are to be true. Power is also related to the effect size. Thus research findings are more likely true in scientific fields with large effects, such as the impact of smoking on cancer or cardiovascular disease (relative risks iii–20), than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases (relative risks 1.i–1.5) [7]. Modern epidemiology is increasingly obliged to target smaller result sizes [sixteen]. Consequently, the proportion of truthful research findings is expected to decrease. In the same line of thinking, if the true event sizes are very small-scale in a scientific field, this field is likely to exist plagued by well-nigh ubiquitous false positive claims. For example, if the majority of true genetic or nutritional determinants of circuitous 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 bottom the choice of tested relationships in a scientific field, the less probable the research findings are to be true. As 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 likely true in confirmatory designs, such every bit large stage III 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 information, such as microarrays and other high-throughput discovery-oriented research [4,viii,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 be "negative" results into "positive" results, i.e., bias, u. For several research designs, e.g., randomized controlled trials [18–20] or meta-analyses [21,22], there have been efforts to standardize their carry 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 mutual when outcomes are unequivocal and universally agreed (e.thousand., decease) rather than when multifarious outcomes are devised (e.one thousand., scales for schizophrenia outcomes) [23]. Similarly, fields that use commonly agreed, stereotyped belittling methods (due east.g., Kaplan-Meier plots and the log-rank test) [24] may yield a larger proportion of true findings than fields where analytical methods are nonetheless nether experimentation (e.g., artificial intelligence methods) and only "best" results are reported. Regardless, fifty-fifty in the most stringent enquiry designs, bias seems to be a major trouble. For case, there is stiff show that selective outcome reporting, with manipulation of the outcomes and analyses reported, is a mutual problem even for randomized trails [25]. Only abolishing selective publication would not brand this trouble go away.

Corollary v: The greater the financial and other interests and prejudices in a scientific field, the less likely the enquiry 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 conventionalities 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 too pb to distorted reported results and interpretations. Prestigious investigators may suppress via the peer review process the advent and dissemination of findings that refute their findings, thus condemning their field to perpetuate false dogma. Empirical evidence on expert opinion shows that it is extremely unreliable [28].

Corollary 6: The hotter a scientific field (with more scientific teams involved), the less probable the research findings are to exist truthful. This seemingly paradoxical corollary follows because, as stated above, the PPV of isolated findings decreases when many teams of investigators are involved in the aforementioned field. This may explain why we occasionally encounter major excitement followed speedily by astringent disappointments in fields that describe wide attention. 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 squad may prioritize on pursuing and disseminating its near 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 be bonny to refute a claim fabricated 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 farthermost opposites is very common in molecular genetics [29].

These corollaries consider each gene separately, merely these factors often influence each other. For example, investigators working in fields where true effect sizes are perceived to be modest may be more likely to perform large studies than investigators working in fields where truthful outcome 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 potent 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 issue in such a big yield of significant relationships that investigators accept enough to report and search further and thus refrain from data dredging and manipulation.

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

In the described framework, a PPV exceeding fifty% is quite difficult to get. Table 4 provides the results of simulations using the formulas adult for the influence of power, ratio of true to non-truthful relationships, and bias, for various types of situations that may be characteristic of specific study designs and settings. A finding from a well-conducted, adequately powered randomized controlled trial starting with a 50% pre-written report run a risk that the intervention is effective is eventually true most 85% of the time. A fairly similar performance is expected of a confirmatory meta-analysis of proficient-quality randomized trials: potential bias probably increases, merely power and pre-test chances are college compared to a unmarried randomized trial. Conversely, a meta-analytic finding from inconclusive studies where pooling is used to "right" the depression power of unmarried studies, is probably faux if R ≤ 1:iii. Research findings from underpowered, early-stage clinical trials would be true almost ane in 4 times, or even less frequently if bias is present. Epidemiological studies of an exploratory nature perform even worse, especially when underpowered, but even well-powered epidemiological studies may take but a 1 in five chance being true, if R = 1:10. Finally, in discovery-oriented research with massive testing, where tested relationships exceed true ones i,000-fold (e.g., 30,000 genes tested, of which 30 may be the true culprits) [thirty,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 Research Findings May Oft Exist Only Accurate Measures of the Prevailing Bias

Equally shown, the majority of mod biomedical inquiry is operating in areas with very depression pre- and post-study probability for true findings. Permit us suppose that in a research field there are no true findings at all to exist discovered. History of scientific discipline teaches us that scientific endeavour has frequently in the past wasted try in fields with absolutely no yield of true scientific information, at least based on our current agreement. In such a "null field," one would ideally expect all observed result sizes to vary by take chances effectually the null in the absence of bias. The extent that observed findings deviate from what is expected by chance lone would be just a pure measure of the prevailing bias.

For example, let us suppose that no nutrients or dietary patterns are actually important determinants for the risk of developing a specific tumor. Let u.s.a. also 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 1.iv for the comparison of the upper to lower intake tertiles. Then the claimed issue sizes are just measuring null else but the cyberspace bias that has been involved in the generation of this scientific literature. Claimed issue sizes are in fact the most authentic estimates of the net bias. It fifty-fifty follows that between "zip fields," the fields that claim stronger effects (often with accompanying claims of medical or public wellness importance) are simply those that have sustained the worst biases.

For fields with very low PPV, the few true relationships would not misconstrue this overall picture much. Fifty-fifty if a few relationships are true, the shape of the distribution of the observed effects would yet yield a clear mensurate of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed big and highly significant effects with excitement, as signs of important discoveries. Too large and as well highly significant effects may actually be more likely to exist signs of large bias in most fields of mod 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 "null field." However, other lines of evidence, or advances in technology 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 exist the range of bias operating in other fields where similar belittling methods, technologies, and conflicts may be operating.

How Can Nosotros Ameliorate the Situation?

Is it unavoidable that near inquiry findings are imitation, or can nosotros amend the state of affairs? A major problem is that it is impossible to know with 100% certainty what the truth is in any enquiry question. In this regard, the pure "gold" standard is unattainable. Nonetheless, there are several approaches to improve the postal service-study probability.

Amend powered testify, e.thousand., large studies or low-bias meta-analyses, may aid, as information technology comes closer to the unknown "gold" standard. Notwithstanding, large 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 research questions posed in electric current enquiry. Large-scale prove should be targeted for research questions where the pre-study probability is already considerably high, then that a significant inquiry finding will lead to a post-test probability that would be considered quite definitive. Large-scale bear witness is likewise especially indicated when information technology can test major concepts rather than narrow, specific questions. A negative finding can then refute not but a specific proposed claim, 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 inquiry. Moreover, one should be cautious that extremely large studies may be more likely to find a formally statistical significant difference for a trivial effect that is non actually meaningfully different from the null [32–34].

2nd, almost research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of whatsoever single team. What matters is the totality of the evidence. Diminishing bias through enhanced enquiry standards and curtailing of prejudices may besides assist. Still, this may require a modify in scientific mentality that might be difficult to attain. 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 be more feasible than registration of each and every hypothesis-generating experiment. Regardless, fifty-fifty if we practise not see a nifty 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-study odds—where enquiry 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 loftier R values may sometimes then exist ascertained. As described to a higher place, whenever ethically acceptable, large studies with minimal bias should exist performed on research findings that are considered relatively established, to see how often they are indeed confirmed. I suspect several established "classics" will neglect the test [36].

Nevertheless, most new discoveries volition go along to stem from hypothesis-generating research with depression or very low pre-study odds. We should so admit that statistical significance testing in the report of a single study gives just a partial picture, without knowing how much testing has been washed 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 enquiry finding. Even if determining this were feasible, this would not inform us most the pre-study odds. Thus, it is unavoidable that ane should make estimate assumptions on how many relationships are expected to be true amongst those probed beyond the relevant research fields and enquiry designs. The wider field may yield some guidance for estimating this probability for the isolated research project. Experiences from biases detected in other neighboring fields would besides exist useful to depict upon. Fifty-fifty though these assumptions would be considerably subjective, they would nevertheless exist very useful in interpreting research claims and putting them in context.

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