Monday, May 7, 2012

Why Most Published Research Findings Are False

Why Most Published Research Findings Are False

 

PLoS Med. 2005 August; 2(8): e124.
Published online 2005 August 30. doi: 10.1371/journal.pmed.0020124
PMCID: PMC1182327
Why Most Published Research Findings Are False
John P. A. Ioannidis
John P. A. Ioannidis is in the Department of Hygiene and Epidemiology, University of Ioannina School of Medicine, Ioannina, Greece, and Institute for Clinical Research and Health Policy Studies, Department of Medicine, Tufts-New England Medical Center, Tufts University School of Medicine, Boston, Massachusetts, United States of America. E-mail: jioannid@cc.uoi.gr
Competing Interests: The author has declared that no competing interests exist.

Summary
There is increasing concern that most current published research findings are false. The probability that a research claim is true may depend on study 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 research finding is less likely to be 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 there is greater financial and other interest and prejudice; and when more teams are involved in a scientific field in chase of statistical significance. Simulations show that for most study designs and settings, it is more likely for a research claim to be false than true. Moreover, for many current scientific fields, claimed research findings may often be simply accurate measures of the prevailing bias. In this essay, I discuss the implications of these problems for the conduct and interpretation of research.
Published research findings are sometimes refuted by subsequent evidence, with ensuing confusion and disappointment. Refutation and controversy is seen across the range of research designs, from clinical trials and traditional epidemiological studies [1–3] to the most modern molecular research [4,5]. There is increasing concern that in modern research, false findings may be the majority or even the vast majority of published research claims [6–8]. However, this should not be surprising. It can be proven that most claimed research findings are false. Here I will examine the key factors that influence this problem and some corollaries thereof.
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 0.05. 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.g., 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 claim exist, rather than null findings.
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]. 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 about the presence of relationships. Let R be the ratio of the number of “true relationships” to “no relationships” among those tested in the field. R is 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. The pre-study probability of a relationship being true is R/(R + 1). The probability of a study finding a true relationship reflects the power 1 - β (one 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 2 × 2 table are given in Table 1. 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. have called the false 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 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 (1 - β)R > 0.05.
Table 1
Table 1
Research Findings and True Relationships
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. We will try to model these two factors in the context of similar 2 × 2 tables.
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. Let u be the proportion of probed analyses that would not have been “research findings,” but nevertheless end up presented and reported as such, because of bias. 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. In the presence of bias (Table 2), one gets PPV = ([1 - β]R + uβR)/(R + α − βR + uuα + uβR), and PPV decreases with increasing u, unless 1 − β ≤ α, i.e., 1 − β ≤ 0.05 for most 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 different pre-study odds in Figure 1. Conversely, true research findings may occasionally be annulled because 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 there may be conflicts of interest that tend to “bury” significant findings [13]. 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.
Table 2
Table 2
Research Findings and True Relationships in the Presence of Bias
Figure 1
PPV (Probability That a Research Finding Is True) as a Function of the Pre-Study Odds for Various Levels of Bias, u
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. For n independent studies of equal power, the 2 × 2 table is shown in Table 3: PPV = R(1 − βn)/(R + 1 − [1 − α]nRβn) (not considering bias). With increasing number of independent studies, PPV tends to decrease, unless 1 - β < a, i.e., 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 βn is replaced by the product of the terms βi for i = 1 to n, but inferences are similar.
Table 3
Table 3
Research Findings and True Relationships in the Presence of Multiple Studies
Figure 2
PPV (Probability That a Research Finding Is True) as a Function of the Pre-Study Odds for Various Numbers of Conducted Studies, n
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.
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 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 around ten gene polymorphisms among those tested would be truly associated with schizophrenia, with relatively similar odds ratios around 1.3 for the ten or so polymorphisms and with a fairly similar power to identify any of them. Then R = 10/100,000 = 10−4, and the pre-study probability for any polymorphism to be associated with schizophrenia is also R/(R + 1) = 10−4. Let us also suppose that the study has 60% power to find an association with an odds ratio of 1.3 at α = 0.05. Then it can be estimated that if a statistically significant association is found with the p-value barely crossing the 0.05 threshold, the post-study 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 us suppose that the investigators manipulate their design, analyses, and reporting so as 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 plan. Such manipulation could be done, for example, with serendipitous inclusion or exclusion of certain patients or controls, post hoc subgroup analyses, investigation of genetic contrasts that were not originally specified, changes in the disease or control definitions, and various combinations of selective or distorted reporting of the results. Commercially available “data mining” packages actually are proud of their ability 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 only 4.4 × 10−4. 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 1.5 × 10−4, hardly any higher than the probability we had before any 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. Small sample size means smaller power and, for all functions above, the PPV for a true research finding decreases as power decreases towards 1 − β = 0.05. 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 100-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 1.1–1.5) [7]. 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 1.05, genetic or nutritional epidemiology would be largely utopian endeavors.
Corollary 3: The greater the number and the lesser the selection of tested relationships in a scientific field, the less likely the research findings are to 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. 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 conduct and reporting. 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.g., death) rather than when multifarious outcomes are devised (e.g., scales for schizophrenia outcomes) [23]. Similarly, fields that use commonly agreed, stereotyped analytical methods (e.g., Kaplan-Meier plots and the log-rank test) [24] may yield a larger proportion of true findings than fields where analytical methods are still under experimentation (e.g., artificial intelligence methods) and only “best” results are reported. 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. Thus, each team may prioritize on pursuing and disseminating its most impressive “positive” results. “Negative” results may become attractive for dissemination only if some other team has found a “positive” association on the same question. 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]. 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 true effect sizes are perceived to be small may be more likely to perform large studies than investigators working in fields where true effect sizes are perceived to be large. Or prejudice may prevail in a hot scientific field, further undermining the predictive value of its research findings. Highly prejudiced stakeholders may even create a barrier that aborts efforts at obtaining and disseminating opposing results. Conversely, the fact that a field is hot or has strong invested interests may sometimes promote larger studies and improved standards of research, enhancing the predictive value of its research findings. Or massive discovery-oriented testing may result in such a large yield of significant relationships that investigators have enough to report and search further and thus refrain from data dredging and manipulation.
In the described framework, a PPV exceeding 50% is quite difficult to get. Table 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 be characteristic of specific study designs and settings. A finding from a well-conducted, adequately 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-analysis of good-quality randomized trials: potential bias probably increases, but 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 be true about one in four 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 have only a one 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.g., 30,000 genes tested, of which 30 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.
Table 4
Table 4
PPV of Research Findings for Various Combinations of Power (1 - ß), Ratio of True to Not-True Relationships (R), and Bias (u)
As shown, the majority of modern biomedical research is operating in areas with very low pre- and post-study probability for true findings. Let us 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 effort in fields with absolutely no yield of true scientific information, at least based on our current understanding. In such a “null field,” one would ideally expect all observed effect sizes to vary by chance around the null in the absence of bias. The extent that observed findings deviate from what is expected by chance alone would be simply 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 us 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 1.4 for the comparison of the upper to lower intake tertiles. Then the claimed effect 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 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 true 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 clear measure of the biases involved in the field. This concept totally reverses the way we view scientific results. Traditionally, investigators have viewed large and highly significant effects with excitement, as signs of important discoveries. Too large and too highly significant effects may actually be more likely to be signs of large bias in most fields of modern research. They should lead investigators to careful critical thinking about what might have gone wrong with their data, analyses, and results.
Of course, investigators working in any field are likely to resist accepting that the whole field in which they have spent their careers is a “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 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.
Is it unavoidable that most research findings are false, or can we improve the situation? A major problem is that it is impossible to know with 100% certainty what the truth is in any research question. In this regard, the pure “gold” standard is unattainable. However, there are several approaches to improve the post-study probability.
Better powered evidence, e.g., large studies or low-bias meta-analyses, may help, as it comes closer to the unknown “gold” standard. However, 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 current research. Large-scale evidence should be targeted for research questions where the pre-study probability is already considerably high, so that a significant research finding will lead to a post-test probability that would be considered quite definitive. Large-scale evidence is also particularly indicated when it can test major concepts rather than narrow, specific questions. A negative finding can then refute not only a specific proposed claim, but a whole field or considerable portion thereof. Selecting the performance of large-scale studies based on narrow-minded criteria, such as the marketing promotion of a specific drug, is largely wasted research. 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 not really meaningfully different from the null [32–34].
Second, most research questions are addressed by many teams, and it is misleading to emphasize the statistically significant findings of any single team. What matters is the totality of the evidence. Diminishing bias through enhanced research standards and curtailing of prejudices may also help. However, this may require a change in scientific mentality that might be difficult to achieve. In some research designs, efforts may also be more successful with upfront registration of studies, e.g., randomized trials [35]. Registration would pose a challenge for hypothesis-generating 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 see a great deal of progress with registration of studies in other fields, the principles of developing and adhering to a protocol could be more widely borrowed from randomized controlled trials.
Finally, instead of chasing statistical significance, we should improve 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 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. I suspect several established “classics” will fail the test [36].
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.
Abbreviation
PPVpositive predictive value
Footnotes
Citation: Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2(8): e124.
  • Ioannidis JP, Haidich AB, Lau J. Any casualties in the clash of randomised and observational evidence? BMJ. 2001;322:879–880. [PMC free article][PubMed]
  • Lawlor DA, Davey Smith G, Kundu D, Bruckdorfer KR, Ebrahim S. Those confounded vitamins: What can we learn from the differences between observational versus randomised trial evidence? Lancet. 2004;363:1724–1727.[PubMed]
  • Vandenbroucke JP. When are observational studies as credible as randomised trials? Lancet. 2004;363:1728–1731.[PubMed]
  • Michiels S, Koscielny S, Hill C. Prediction of cancer outcome with microarrays: A multiple random validation strategy. Lancet. 2005;365:488–492.[PubMed]
  • Ioannidis JPA, Ntzani EE, Trikalinos TA, Contopoulos-Ioannidis DG. Replication validity of genetic association studies. Nat Genet. 2001;29:306–309.[PubMed]
  • Colhoun HM, McKeigue PM, Davey Smith G. Problems of reporting genetic associations with complex outcomes. Lancet. 2003;361:865–872.[PubMed]
  • Ioannidis JP. Genetic associations: False or true? Trends Mol Med. 2003;9:135–138.[PubMed]
  • Ioannidis JPA. Microarrays and molecular research: Noise discovery? Lancet. 2005;365:454–455.[PubMed]
  • Sterne JA, Davey Smith G. Sifting the evidence—What's wrong with significance tests. BMJ. 2001;322:226–231. [PMC free article][PubMed]
  • Wacholder S, Chanock S, Garcia-Closas M, Elghormli L, Rothman N. Assessing the probability that a positive report is false: An approach for molecular epidemiology studies. J Natl Cancer Inst. 2004;96:434–442.[PubMed]
  • Risch NJ. Searching for genetic determinants in the new millennium. Nature. 2000;405:847–856.[PubMed]
  • Kelsey JL, Whittemore AS, Evans AS, Thompson WD. Methods in observational epidemiology, 2nd ed. New York: Oxford U Press; 1996. 432 pp.
  • Topol EJ. Failing the public health—Rofecoxib, Merck, and the FDA. N Engl J Med. 2004;351:1707–1709.[PubMed]
  • Yusuf S, Collins R, Peto R. Why do we need some large, simple randomized trials? Stat Med. 1984;3:409–422.[PubMed]
  • Altman DG, Royston P. What do we mean by validating a prognostic model? Stat Med. 2000;19:453–473.[PubMed]
  • Taubes G. Epidemiology faces its limits. Science. 1995;269:164–169.[PubMed]
  • Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science. 1999;286:531–537.[PubMed]
  • Moher D, Schulz KF, Altman DG. The CONSORT statement: Revised recommendations for improving the quality of reports of parallel-group randomised trials. Lancet. 2001;357:1191–1194.[PubMed]
  • Ioannidis JP, Evans SJ, Gotzsche PC, O'Neill RT, Altman DG, et al. Better reporting of harms in randomized trials: An extension of the CONSORT statement. Ann Intern Med. 2004;141:781–788.[PubMed]
  • International Conference on Harmonisation E9 Expert Working Group. ICH Harmonised Tripartite Guideline. Statistical principles for clinical trials. Stat Med. 1999;18:1905–1942.[PubMed]
  • Moher D, Cook DJ, Eastwood S, Olkin I, Rennie D, et al. Improving the quality of reports of meta-analyses of randomised controlled trials: The QUOROM statement. Quality of Reporting of Meta-analyses. Lancet. 1999;354:1896–1900.[PubMed]
  • Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, et al. Meta-analysis of observational studies in epidemiology: A proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) group. JAMA. 2000;283:2008–2012.[PubMed]
  • Marshall M, Lockwood A, Bradley C, Adams C, Joy C, et al. Unpublished rating scales: A major source of bias in randomised controlled trials of treatments for schizophrenia. Br J Psychiatry. 2000;176:249–252.[PubMed]
  • Altman DG, Goodman SN. Transfer of technology from statistical journals to the biomedical literature. Past trends and future predictions. JAMA. 1994;272:129–132.[PubMed]
  • Chan AW, Hrobjartsson A, Haahr MT, Gotzsche PC, Altman DG. Empirical evidence for selective reporting of outcomes in randomized trials: Comparison of protocols to published articles. JAMA. 2004;291:2457–2465.[PubMed]
  • Krimsky S, Rothenberg LS, Stott P, Kyle G. Scientific journals and their authors' financial interests: A pilot study. Psychother Psychosom. 1998;67:194–201.[PubMed]
  • Papanikolaou GN, Baltogianni MS, Contopoulos-Ioannidis DG, Haidich AB, Giannakakis IA, et al. Reporting of conflicts of interest in guidelines of preventive and therapeutic interventions. BMC Med Res Methodol. 2001;1:3. [PMC free article][PubMed]
  • Antman EM, Lau J, Kupelnick B, Mosteller F, Chalmers TC. A comparison of results of meta-analyses of randomized control trials and recommendations of clinical experts. Treatments for myocardial infarction. JAMA. 1992;268:240–248.[PubMed]
  • 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. 2005;58:543–549.[PubMed]
  • Ntzani EE, Ioannidis JP. Predictive ability of DNA microarrays for cancer outcomes and correlates: An empirical assessment. Lancet. 2003;362:1439–1444.[PubMed]
  • Ransohoff DF. Rules of evidence for cancer molecular-marker discovery and validation. Nat Rev Cancer. 2004;4:309–314.[PubMed]
  • Lindley DV. A statistical paradox. Biometrika. 1957;44:187–192.
  • Bartlett MS. A comment on D.V. Lindley's statistical paradox. Biometrika. 1957;44:533–534.
  • Senn SJ. Two cheers for P-values. J Epidemiol Biostat. 2001;6:193–204.[PubMed]
  • De Angelis C, Drazen JM, Frizelle FA, Haug C, Hoey J, et al. Clinical trial registration: A statement from the International Committee of Medical Journal Editors. N Engl J Med. 2004;351:1250–1251.[PubMed]
  • Ioannidis JPA. Contradicted and initially stronger effects in highly cited clinical research. JAMA. 2005;294:218–228.[PubMed]
  • Hsueh HM, Chen JJ, Kodell RL. Comparison of methods for estimating the number of true null hypotheses in multiplicity testing. J Biopharm Stat. 2003;13:675–689.[PubMed]

"We will soon find ourselves plagued by new forms of distress. No, it's not the economy. It's not that we are all becoming socially isolated ..." - Psychiatrists Are About to Shift the Boundaries Between Sane and Insane - Scientific American (blog)

psychiatry - Google Search


News for psychiatry

  1. Psychiatrists Are About to Shift the Boundaries Between Sane and Insane

    Scientific American (blog)‎ - 40 minutes ago
    We will soon find ourselves plagued by new forms of distress. No, it's not the economy. It's not that we are all becoming socially isolated ...
  1. Philadelphia Inquirer‎ - 1 day ago
  2. Sydney Morning Herald‎ - 23 hours ago

Sunday, May 6, 2012

Forensic psychiatric evaluation, NGRI standards and unitary theory of mental illness

Forensic psychiatric evaluation, NGRI standards and unitary theory of mental illness


Forensic psychiatric evaluation, NGRI standards and unitary theory of mental illness

Unitary Theory of Mental Illness - Topic Updates from Behavior and Law

*

http://www.springerlink.com/content/g9v42351550135ur/

Volume 10, Number 4 (1959), 13-19, DOI: 10.1007/BF01741038


Abstract

An address presented to the Second International Congress of Psychiatry in Zurich, Switzerland, September 4, 1957. A portion of a book on psychiatric diagnosis now in preparation to be published by Harcourt, Brace & Company. Reprinted from theBull. Menninger Clin. 22:4–12, 1958, by permission.

Fulltext Preview

Image of the first page of the fulltext document 
*
Front Cover







2 ReviewsWrite review

General Psychopathology, Volume 2 

By Karl Jaspers, J. Hoenig, Marian W. Hamilton
*


*
Mecacci L.
Cortex. 2005 Dec;41(6):816-22.
PMID:
16350662
[PubMed - indexed for MEDLINE]

Neuroimage evidence and the insanity defense - Unitary Theory of Mental Illness

Google Reader - Unitary Theory of Mental Illness

via pubmed: ngri standards by Schweitzer NJ, Saks MJ on 5/6/12
Neuroimage evidence and the insanity defense.
Behav Sci Law. 2011 Jul;29(4):592-607
Authors: Schweitzer NJ, Saks MJ
Abstract
The introduction of neuroscientific evidence in criminal trials has given rise to fears that neuroimagery presented by an expert witness might inordinately influence jurors' evaluations of the defendant. In this experiment, a diverse sample of 1,170 community members from throughout the U.S. evaluated a written mock trial in which psychological, neuropsychological, neuroscientific, and neuroimage-based expert evidence was presented in support of a not guilty by reason of insanity (NGRI) defense. No evidence of an independent influence of neuroimagery was found. Overall, neuroscience-based evidence was found to be more persuasive than psychological and anecdotal family history evidence. These effects were consistent across different insanity standards. Despite the non-influence of neuroimagery, however, jurors who were not provided with a neuroimage indicated that they believed neuroimagery would have been the most helpful kind of evidence in their evaluations of the defendant.
PMID: 21744379 [PubMed - indexed for MEDLINE]

pubmed: ngri standards

pubmed: ngri standards

You are viewing a feed that contains frequently updated content. When you subscribe to a feed, it is added to the Common Feed List. Updated information from the feed is automatically downloaded to your computer and can be viewed in Internet Explorer and other programs. Learn more about feeds.



Neuroimage evidence and the insanity defense.

Schweitzer NJ, Saks MJGo to full article
Neuroimage evidence and the insanity defense.
Behav Sci Law. 2011 Jul;29(4):592-607
Authors: Schweitzer NJ, Saks MJ
Abstract
The introduction of neuroscientific evidence in criminal trials has given rise to fears that neuroimagery presented by an expert witness might inordinately influence jurors' evaluations of the defendant. In this experiment, a diverse sample of 1,170 community members from throughout the U.S. evaluated a written mock trial in which psychological, neuropsychological, neuroscientific, and neuroimage-based expert evidence was presented in support of a not guilty by reason of insanity (NGRI) defense. No evidence of an independent influence of neuroimagery was found. Overall, neuroscience-based evidence was found to be more persuasive than psychological and anecdotal family history evidence. These effects were consistent across different insanity standards. Despite the non-influence of neuroimagery, however, jurors who were not provided with a neuroimage indicated that they believed neuroimagery would have been the most helpful kind of evidence in their evaluations of the defendant.
PMID: 21744379 [PubMed - indexed for MEDLINE]

Highlights From APA's 2012 Annual Meeting - General Psychiatry News

Google Reader - General Psychiatry News

General Psychiatry News

"General Psychiatry News" bundle created by Mike Nova

A bundle is a collection of blogs and websites hand-selected by your friend on a particular topic or interest. You can keep up to date with them all in one place by subscribing in Google Reader.
There are
71 feeds
included in this bundle
  • The American Journal of Psychiatry Current Issue
  • Academic Psychiatry Current Issue
  • Archives of General Psychiatry current issue
  • The British Journal of Psychiatry current issue
  • candidaabrahamson
  • Journal of Abnormal Psychology - Vol 121, Iss 1
  • Psychopathology : Last 20 articles
  • Psychology, Philosophy and Real Life
  • Evid. Based Ment. Health: Most-Read Full-Text Articles
  • Psychiatric News Alert
  • Journal of Psychiatric Research - ScienceDirect Publication
  • Psychiatry Research - ScienceDirect Publication
  • FOCUS Current Issue
  • Uploads by NIMHgov
  • IRP - International Review of Psychiatry: Table of Contents
  • JAMA current issue
  • American Journal of Geriatric Psych - Featured Articles - Featured Articles
  • Psychiatric Genetics - Current Issue
  • Medicine JournalFeeds » Psychiatry
  • Mental Health Writers' Guild
  • The Journal of Neuropsychiatry and Clinical Neurosciences Current Issue
  • international psychiatry - Google News
  • international psychiatry journals - Google News
  • psychiatric diagnosis - Google News
  • psychiatric journal articles
  • psychiatry research - Google News
  • Genes, Brain and Behavior
  • British Journal of Clinical Psychology
  • PsychiatryOnline |
  • Acta Psychiatrica Scandinavica - Journal Information
  • World psychiatry: official journal of the World Psychiatric Association (WPA) | ResearchGate
  • Home | psychiatry.org
  • PsychiatryOnline | Topics | Forensic Psychiatry
  • Medscape - Psychiatry Journals
  • Biological Psychiatry - Elsevier
  • Psychiatric Services Current Issue
  • Schizophrenia Bulletin - current issue
  • NYT > Psychiatry and Psychiatrists
  • NYT > Psychology and Psychologists
  • Twitter / NMD_online
  • NIMH - Twitter
  • Twitter / APAPsychiatric
  • Annals of General Psychiatry - Latest Articles
  • Annals of General Psychiatry - Latest Comments
  • Annals of General Psychiatry - Last 30 days most viewed article(s)
  • Archives of GP-Most accessed articles
  • Archives of GP-Latest Articles
  • BMC - Latest Articles
  • EM-Consulte - European Psychiatry - Mise à jour
  • psychiatry research - Google Blog Search
  • international psychiatry - Google Blog Search
  • international psychiatry journals - Google Blog Search
  • psychiatric diagnosis - Google Blog Search
  • Industrial Psychiatry Journal : 2010 - 19(2)
  • Journal of the American Academy of Child & Adolescent Psychiatry
  • Journal of the American Academy of Child & Adolescent Psychiatry - Articles in Press
  • Medscape Psychiatry
  • National Association of Social Workers
  • NIMH | Director’s Blog
  • NIMH | Audio
  • NIMH | Video
  • NIMH | Recent Updates
  • NYT > Health
  • Psychiatric Diagnosis - from OpenDB
  • Psychiatric Clinics of North America
  • Psychiatric Clinics of North America - Articles in Press
  • Psychiatric Times
  • DSM5 in Distress
  • Psychiatric Quarterly (Browse Results)
  • The Lancet
  • Epilepsia


Author: Diana Mahoney Publication: Clinical Psychiatry News (Magazine/Journal) Date: February 1, 2008. Publisher: International Medical News Group Volume: 36 Issue: 2 Page: 28(1) Distributed by Gale, a part of Cengage ...

A psychoanalytic slant on the world…with support from the American Psychoanalytic Foundation ... International Psychoanalysis home page ... Click Here to Read: Psychiatry's bible, the DSM, is doing more harm than good ...

Meet fellow attendees, exchange ideas and experiences, learn from a comprehensive scientific program, promote and advocate for your research and ideas, and enjoy all that Calgary, Alberta and Canada has to offer.

via DSM5 in Distress by Allen J. Frances, M.D. on 5/6/12
The whole purpose of having a manual of psychiatric diagnosis is to promote diagnostic agreement. DSM III was an important milestone because it saved the credibility and relevance of psychiatry at a time when it was ridiculed for low reliability.
read more


Overdue babies risk behavioural problems—research
The Zimbabwe Standard
Women should be aware of the risks of prolonging pregnancy, experts report in the International Journal of Epidemiology. The research was carried out in The Netherlands, where until recently it was commonplace for women to choose not to be induced if ...

and more »


Suicides abroad hard to explain
Tucson Citizen
USA TODAY spent two months investigating suicide abroad, tabulating 10 years of State Department data, searching newspapers throughout the world, reviewing thousands of studies in professional journals and interviewing psychologists, sociologists, ...

and more »

via Home | psychiatry.org on 5/6/12
from the Pennsylvania Convention Center, May 4 – 9, featuring interviews, coverage of special events, and Thought Leadership segments from leading university psychiatry programs. Also see the Daily Bulletin.

This post has been generated by Page2RSS


Doctors need to address ageing workforce
Sky News Australia
George Skowronski from St George Hospital Sydney and Carmelle Peisah from the University of NSW's School of Psychiatry said strategies were needed to retain older doctors in a safe and appropriate manner. 'Changes in processing speed and memory are ...

and more »

via Psychiatric News Alert by noreply@blogger.com (Psychiatric News Alert) on 5/6/12
Here's more news from APA's 2012 annual meeting, which is being held in Philadelphia now through May 9.


Psychotherapy Useful at End of Life, Says Expert
In this video, Lorenzo Norris, M.D., discusses methods for providing palliative psychotherapy to terminally ill patients. Norris, an assistant professor of psychiatry, associate residency director, and director of the Psychiatric Consultation-Liaison Service in the Department of Psychiatry and Behavioral Services at the George Washington University Medical Center, spoke at APA’s 2012 annual meeting. Watch the video.

Technology is changing the way information—including information vital to psychiatrists wanting to stay abreast of science relevant to quality clinical care—is disseminated, said Robert Hales, M.D., editor in chief for books at American Psychiatric Publishing (APP), at APA’s 2012 annual meeting in Philadelphia. He was the winner of APA’s 2012 Judd Marmor Award.

Assembly Elects New Officers
At today's meeting of the APA Assembly, the delegates voted to make Assembly Recorder Melinda Young, M.D., of California the next speaker-elect. They also chose Jenny Boyer, M.D., to succeed Young as recorder. At the end of the three-day meeting, Scott Benson, M.D., of Florida became the new Assembly speaker.
For previous news alerts, click here.


Overdue babies risk behavioural problems—research
The Zimbabwe Standard
Lead researcher Dr Hanan El Marroun from the Department of Child and Adolescent Psychiatry at Erasmus MC-Sophia in Rotterdam said post-term as well as pre-term births seemed to be associated with long-term health effects. She said “Every pregnant woman ...

and more »


Multiple thought channels may help brain avoid traffic jams
EurekAlert (press release)
"Examining the temporal structure of brain activity from this perspective may be especially helpful in understanding psychiatric conditions like depression and schizophrenia, where structural markers are scarce." The research will be published May 6 in ...

and more »

Psychiatric News by David J. Kupfer, M.D.. May 4, 2012. As of this month, the 12-month countdown to the release of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders [DSM-5] officially begins.

via psychiatric diagnosis - Google Blog Search by Typed ROBIN on 5/6/12
By Maia Szalavitz | @maiasz | May 3, 2012 | + The committee responsible for revising the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders — psychiatry's diagnostic “bible” commonly referred to as the ...

Each received the psychiatric diagnosis and military discharge after reporting a sexual assault. I'm not crazy. I am actually relatively normal.Stephanie Schroeder. “I'm not crazy,” says Schroeder, who is married now, with two ...

The “disorders” in the diagnostic manual are invented by psychiatrists and placed in the DSM for the sole purpose of increasing the numbers of diagnosis that can be made. Vancouver-, British Columbia, Canada., May 5, 2012 ...


Babble (blog)

DSM-5 Changes Aren't Limited to Autism, Final Feedback Sought
Babble (blog)
The American Psychiatric Association is seeking final input on the drafted changes to the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The public can give feedback on the changes from now until June 15 through the ...

and more »

via Psychiatric News Alert by noreply@blogger.com (Psychiatric News Alert) on 5/6/12
APA's 2012 annual meeting is taking place in Philadelphia. From now through the end of the meeting on May 9, you will receive highlights of meeting events and scientific sessions.

Complexities of Therapeutic Process Raise Difficult Issues
When should transference work be used in psychodynamic psychotherapy? That was one of the questions addressed by Glen O. Gabbard, M.D., in a master course titled “Challenges in Psychodynamic Psychotherapy” at APA’s 2012 annual meeting in Philadelphia today.

Findings on Deep Brain Stimulation Open Doors to Rethinking Depression Treatment
Deep brain stimulation (DBS) may be an effective intervention for treatment-resistant depression, offering a way to reconceptualize depression and its treatment, said Helen Mayberg, M.D., at APA’s annual meeting today. She was one of the meeting’s “Frontiers in Science” lecturers.


Holistic Approach to Youth With Psychosis, Substance Abuse Promotes Recovery
A holistic approach to the treatment of young people who have experienced a first episode of psychosis and who also have co-occurring alcohol and substance abuse disorders may prevent years of disability. Robert Drake, M.D., Ph.D., expounded on that theme at APA's 2012 annual meeting.

Get Expert Guidance on CBT
In this video, Jesse Wright, M.D., discusses his work in cognitive-behavior therapy and the books he has cowritten for American Psychiatric Publishing. To learn more, click on the headline and watch the video.
For previous news alerts, click here.

via Mental Health Writers' Guild by boldkevin on 5/6/12
The following comment was received from Candida Abrahamson and I thought I would feature it here so that it did not get missed.
Many thanks to Candida for reminding us….
Just reminding everyone that May is Mental Health Awareness Month, with two main areas of focus:
1. “Do More for 1 in 4″: A call to help the one in four people with a diagnosable mental illness; and
2. “Healing Trauma’s Invisible Wounds,” looking at the impact of traumatic events on both individuals and communities at large.
Please see Mental Health America for details and downloadable tool kits. Best, Candida
[Editor's Comment: Additional resources available for download are available from the Mental Health America site. I would very much like to add my encouragement to those of Candida's for members to pop across and check it out and to download those resources for themselves.]


via candidaabrahamson by candidaabrahamson on 5/6/12
A reminder that we’re in Mental Health Awareness Month, run by Mental Health America (formerly the National Mental Health Association), which is the country’s leading nonprofit dedicated to mental health. This year awareness surrounds two main themes: Do More for 1 in 4: a “call to action to help the 1 in 4 American adults who … Continue reading »

Sokratis E Karaoulanis, Alexandros Daponte, Katerina A Rizouli, Andreas A Rizoulis, Georgios A Lialios, Catherine T Theodoridou, Christos Christakopoulos, Nikiforos V Angelopoulos
Annals of General Psychiatry 2012, 11:9 (10 April 2012)

Tine K Grimholt, Mari A Bjornaas, Dag Jacobsen, Gudrun Dieserud, Oivind Ekeberg
Annals of General Psychiatry 2012, 11:10 (20 April 2012)

Is homophobia associated with homosexual ar... [J Abnorm Psychol. 1996] - PubMed - NCBI

Is homophobia associated with homosexual ar... [J Abnorm Psychol. 1996] - PubMed - NCBI

J Abnorm Psychol. 1996 Aug;105(3):440-5.

Is homophobia associated with homosexual arousal?

Source

Department of Psychology, University of Georgia, Athens 30602-3013, USA.

Abstract

The authors investigated the role of homosexual arousal in exclusively heterosexual men who admitted negative affect toward homosexual individuals. Participants consisted of a group of homophobic men (n = 35) and a group of nonhomophobic men (n = 29); they were assigned to groups on the basis of their scores on the Index of Homophobia (W. W. Hudson & W. A. Ricketts, 1980). The men were exposed to sexually explicit erotic stimuli consisting of heterosexual, male homosexual, and lesbian videotapes, and changes in penile circumference were monitored. They also completed an Aggression Questionnaire (A. H. Buss & M. Perry, 1992). Both groups exhibited increases in penile circumference to the heterosexual and female homosexual videos. Only the homophobic men showed an increase in penile erection to male homosexual stimuli. The groups did not differ in aggression. Homophobia is apparently associated with homosexual arousal that the homophobic individual is either unaware of or denies.

PMID:
8772014
[PubMed - indexed for MEDLINE]