Several methodologists have pointed out [ 9—11 ] that the high rate of nonreplication lack of confirmation of research discoveries is a consequence of the convenient, yet 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.
Research is not most appropriately represented and summarized by p -values, but, unfortunately, there is a widespread notion that medical research articles should be interpreted based only on p -values. Research findings are defined here as any relationship reaching formal statistical significance, e.
However, here we will target relationships that investigators claim exist, rather than null findings. As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true before doing the study , the statistical power of the study, and the level of statistical significance [ 10 , 11 ].
In a research field both true and false hypotheses can be made about the presence of relationships. 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 true relationships among thousands and millions of hypotheses that may be postulated.
Let us also consider, for computational simplicity, circumscribed fields where either there is only one true relationship among many that can be hypothesized or the power is similar to find any of the several existing true relationships. After 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. What is less well appreciated is that bias and the extent of repeated independent testing by different teams of investigators around the globe may further distort this picture and may lead to even smaller probabilities of the research findings being indeed true. First, let us 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.
Bias should not be confused with chance variability that causes some findings to be false by chance even though the study design, data, analysis, 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 relationship exists or not. This is not an unreasonable assumption, since typically it is impossible to know which relationships are indeed true.
Thus, with increasing bias, the chances that a research finding is true diminish considerably. This is shown for different levels of power and for different pre-study odds in Figure 1. Conversely, true research findings may occasionally be annulled because of reverse bias.
There is no good large-scale empirical evidence on how frequently such reverse bias may occur across diverse research 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 becoming 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 way as bias above. Also reverse bias should not be confused with chance variability that may lead to missing a true relationship because of chance.
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 same or similar questions.
Unfortunately, in some areas, the prevailing mentality until now has been to focus on isolated discoveries by single teams and interpret research experiments in isolation. An increasing number of questions have at least one study claiming a research finding, and this receives unilateral attention.
The probability that at least one study, among several done on the same question, claims a statistically significant research finding is easy to estimate.
This is shown for different levels of power and for different pre-study odds in Figure 2. A practical example is shown in Box 1. Based on the above considerations, one may deduce several interesting corollaries about the probability that a research finding is indeed true. Let us assume that a team of investigators performs a whole genome association study to test whether any of , 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 around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.
Then it can be estimated that if a statistically significant association is found with the p -value barely crossing the 0. 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.
Furthermore, even in the absence of any bias, when ten independent research teams perform similar experiments around the world, if one of them finds a formally statistically significant association, the probability that the research finding is true is only 1. Corollary 1: The smaller the studies conducted in a scientific field, the less likely the research findings are to be true. Thus, other factors being equal, research findings are more likely true in scientific fields that undertake large studies, such as randomized controlled trials in cardiology several thousand subjects randomized [ 14 ] than in scientific fields with small studies, such as most research of molecular predictors sample sizes fold smaller [ 15 ].
Corollary 2: The smaller the effect 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 3—20 , than in scientific fields where postulated effects are small, such as genetic risk factors for multigenetic diseases relative risks 1.
Modern epidemiology is increasingly obliged to target smaller effect sizes [ 16 ]. Consequently, the proportion of true research findings is expected to decrease. In the same line of thinking, if the true effect sizes are very small in a scientific field, this field is likely to be plagued by almost ubiquitous false positive claims.
For example, if the majority of true genetic or nutritional determinants of complex diseases confer relative risks less than 1. Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to be true. As shown above, the post-study 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 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 , 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. For several research designs, e. Adherence to common standards is likely to increase the proportion of true findings. The same applies to outcomes. True findings may be more common when outcomes are unequivocal and universally agreed e.
Similarly, fields that use commonly agreed, stereotyped analytical methods e. Regardless, even in the most stringent research 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 problem even for randomized trails [ 25 ].
Simply abolishing selective publication would not make this problem 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 also lead to distorted reported results and interpretations.
Prestigious investigators may suppress via the peer review process the appearance 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 likely the research 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 see major excitement followed rapidly by severe disappointments in fields that draw wide attention.
With many teams working on the same field and with massive experimental data being produced, timing is of the essence in beating competition. In that case, it may be attractive to refute a claim made in some prestigious journal. The term Proteus phenomenon has been coined to describe this phenomenon of rapidly alternating extreme research claims and extremely opposite refutations [ 29 ]. Bias can entail manipulation in the reports of findings or being selective in the findings.
Especially when it comes to controversial research or a trending topic in society. If possible, see who funded the study as well. Science is self-correcting and eventually, the real truth comes out. Reviewing papers that actually conducted the study is far more likely to give you correct information versus a random blog post regurgitating the same information.
Keep that in mind for when you write your next biology report. It will help get you the grade you deserve! Save my name, email, and website in this browser for the next time I comment. Studies Need More than Statistical Significance The process of testing a hypothesis, or what researchers refer to as the p-value, is another factor leading to false published research findings.
Conflicts of Interest or Biases Sway Findings Conflicts of interests or biases are another leading cause as to why many published research findings are false. 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 be ascertained. As described above, whenever ethically acceptable, large studies with minimal bias should be performed on research findings that are considered relatively established, to see how often they are indeed confirmed. Nevertheless, most new discoveries will continue to stem from hypothesis-generating research with low or very low pre-study odds.
We should then acknowledge that statistical significance testing in the report of a single study gives only a partial picture, 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 not inform us about the pre-study odds. Thus, it is unavoidable that one should make approximate assumptions on how many relationships are expected to be true among those probed across the relevant research fields and research 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 also be useful to draw upon. Even though these assumptions would be considerably subjective, they would still be very useful in interpreting research claims and putting them in context. Abstract Summary There is increasing concern that most current published research findings are false.
Abbreviation: PPV, positive predictive value. Modeling the Framework for False Positive Findings Several methodologists have pointed out [ 9—11 ] that the high rate of nonreplication lack of confirmation of research discoveries is a consequence of the convenient, yet 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. It can be proven that most claimed research findings are false As has been shown previously, the probability that a research finding is indeed true depends on the prior probability of it being true before doing the study , the statistical power of the study, and the level of statistical significance [ 10 , 11 ].
Download: PPT. Bias First, let us 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. Figure 1. Table 2. Testing by Several Independent Teams Several independent teams may be addressing the same sets of research questions. Figure 2. Table 3. Corollaries A practical example is shown in Box 1. Box 1. An Example: Science at Low Pre-Study Odds Let us assume that a team of investigators performs a whole genome association study to test whether any of , gene polymorphisms are associated with susceptibility to schizophrenia.
Table 4. 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-study probability for true findings. How Can We Improve the Situation? References 1. BMJ — View Article Google Scholar 2. Lancet — View Article Google Scholar 3. Vandenbroucke JP When are observational studies as credible as randomised trials? View Article Google Scholar 4. View Article Google Scholar 5.
Nat Genet — View Article Google Scholar 6. View Article Google Scholar 7. Ioannidis JP Genetic associations: False or true? Trends Mol Med 9: — View Article Google Scholar 8. View Article Google Scholar 9. View Article Google Scholar J Natl Cancer Inst — Risch NJ Searching for genetic determinants in the new millennium.
Nature — New York: Oxford U Press. N Engl J Med — Stat Med 3: — Stat Med — Taubes G Epidemiology faces its limits. Science — Ann Intern Med — Statistical principles for clinical trials. Quality of Reporting of Meta-analyses. JAMA — Br J Psychiatry — Past trends and future predictions. Psychother Psychosom — Treatments for myocardial infarction. Ioannidis JP, Trikalinos TA Early extreme contradictory estimates may appear in published research: The Proteus phenomenon in molecular genetics research and randomized trials.
J Clin Epidemiol — Ransohoff DF Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer 4: — Lindley DV A statistical paradox. Biometrika — Bartlett MS A comment on D. Lindley's statistical paradox. Senn SJ Two cheers for P-values.
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