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Search Results: 1 - 10 of 156 matches for " Ioannidis "
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Is Public Co-Ordination of Investment in Information Security Desirable?  [PDF]
Christos Ioannidis, David Pym, Julian Williams
Journal of Information Security (JIS) , 2016, DOI: 10.4236/jis.2016.72005
Abstract: This paper provides for the presentation, in an integrated manner, of a sequence of results addressing the consequences of the presence of an information steward in an ecosystem under attack and establishes the appropriate defensive investment responses, thus allowing for a cohesive understanding of the nature of the information steward in a variety of attack contexts. We determine the level of investment in information security and attacking intensity when agents react in a non-coordinated manner and compare them to the case of the system’s coordinated response undertaken under the guidance of a steward. We show that only in the most well-designed institutional set-up the presence of the well-informed steward provides for an increase of the system’s resilience to attacks. In the case in which both the information available to the steward and its policy instruments are curtailed, coordinated policy responses yield no additional benefits to individual agents and in some case they actually compared unfavourably to atomistic responses. The system’s sustainability does improve in the presence of a steward, which deters attackers and reduces the numbers and intensity of attacks. In most cases, the resulting investment expenditure undertaken by the agents in the ecosystem exceeds its Pareto efficient magnitude.
Why Most Published Research Findings Are False: Author's Reply to Goodman and Greenland
John P. A Ioannidis
PLOS Medicine , 2007, DOI: 10.1371/journal.pmed.0040215
Abstract:
Why Most Published Research Findings Are False
John P. A. Ioannidis
PLOS Medicine , 2005, DOI: 10.1371/journal.pmed.0020124
Abstract: 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.
Author's Reply
John P. A Ioannidis
PLOS Medicine , 2005, DOI: 10.1371/journal.pmed.0020398
Abstract:
Measuring Co-Authorship and Networking-Adjusted Scientific Impact
John P. A. Ioannidis
PLOS ONE , 2008, DOI: 10.1371/journal.pone.0002778
Abstract: Appraisal of the scientific impact of researchers, teams and institutions with productivity and citation metrics has major repercussions. Funding and promotion of individuals and survival of teams and institutions depend on publications and citations. In this competitive environment, the number of authors per paper is increasing and apparently some co-authors don't satisfy authorship criteria. Listing of individual contributions is still sporadic and also open to manipulation. Metrics are needed to measure the networking intensity for a single scientist or group of scientists accounting for patterns of co-authorship. Here, I define I1 for a single scientist as the number of authors who appear in at least I1 papers of the specific scientist. For a group of scientists or institution, In is defined as the number of authors who appear in at least In papers that bear the affiliation of the group or institution. I1 depends on the number of papers authored Np. The power exponent R of the relationship between I1 and Np categorizes scientists as solitary (R>2.5), nuclear (R = 2.25–2.5), networked (R = 2–2.25), extensively networked (R = 1.75–2) or collaborators (R<1.75). R may be used to adjust for co-authorship networking the citation impact of a scientist. In similarly provides a simple measure of the effective networking size to adjust the citation impact of groups or institutions. Empirical data are provided for single scientists and institutions for the proposed metrics. Cautious adoption of adjustments for co-authorship and networking in scientific appraisals may offer incentives for more accountable co-authorship behaviour in published articles.
Concentration of the Most-Cited Papers in the Scientific Literature: Analysis of Journal Ecosystems
John P. A. Ioannidis
PLOS ONE , 2006, DOI: 10.1371/journal.pone.0000005
Abstract: Background A minority of scientific journals publishes the majority of scientific papers and receives the majority of citations. The extent of concentration of the most influential articles is less well known. Methods/Principal Findings The 100 most-cited papers in the last decade in each of 21 scientific fields were analyzed; fields were considered as ecosystems and their “species” (journal) diversity was evaluated. Only 9% of journals in Journal Citation Reports had published at least one such paper. Among this 9%, half of them had published only one such paper. The number of journals that had published a larger number of most-cited papers decreased exponentially according to a Lotka law. Except for three scientific fields, six journals accounted for 53 to 94 of the 100 most-cited papers in their field. With increasing average number of citations per paper (citation density) in a scientific field, concentration of the most-cited papers in a few journals became even more prominent (p<0.001). Concentration was unrelated to the number of papers published or number of journals available in a scientific field. Multidisciplinary journals accounted for 24% of all most-cited papers, with large variability across fields. The concentration of most-cited papers in multidisciplinary journals was most prominent in fields with high citation density (correlation coefficient 0.70, p<0.001). Multidisciplinary journals had published fewer than eight of the 100 most-cited papers in eight scientific fields (none in two fields). Journals concentrating most-cited original articles often differed from those concentrating most-cited reviews. The concentration of the most-influential papers was stronger than the already prominent concentration of papers published and citations received. Conclusions Despite a plethora of available journals, the most influential papers are extremely concentrated in few journals, especially in fields with high citation density. Existing multidisciplinary journals publish selectively most-cited papers from fields with high citation density.
Materializing research promises: opportunities, priorities and conflicts in translational medicine
John PA Ioannidis
Journal of Translational Medicine , 2004, DOI: 10.1186/1479-5876-2-5
Abstract: The status of translational research has drawn increasing attention recently in top biomedical journals [1-5] and in the policy making of the NIH, as reflected also in the NIH Roadmap [6]. Translational medicine encompasses all the disciplines that intervene in moving scientific progress from the bench to the bedside and in conveying stimulating information from the bedside back to the bench [5]. While basic sciences are conceived as having made amazing leaps forward, this progress has not resulted in many major cures [7]. At the other end, clinicians are considered too unfamiliar with the capacities of modern science to bring fruitful questions to the attention of basic scientists [5]. Nevertheless, recent evolutions in basic and clinical science have created a new window of opportunity for the growth of translational medicine. The aim of this commentary is to discuss why translational efforts might have failed to-date, how this new opportunity may be best exploited, and some remaining obstacles that must be overcome.My team recently examined the rate of translation of promising basic research findings to clinical applications [8]. We screened reports published between 1979–1983 in 6 top basic science journals (Science, Nature, Cell, Journal of Biological Chemistry, Journal of Experimental Medicine, and Journal of Clinical Investigation). We found 101 articles that clearly made a promise for a major clinical application of their findings. Two decades later, only 5 of these promises were in licensed clinical use and only one of them had a major impact on current medical practice. Three quarters of the basic science promises had not yet been tested in a randomized trial. The strongest predictor of moving to randomized experimentation was industry involvement in the original basic science publication. In the absence of such involvement, scientists could not see their findings materialize (figure 1). Since the analyzed publications represented very early stage basic re
Effectiveness of antidepressants: an evidence myth constructed from a thousand randomized trials?
John PA Ioannidis
Philosophy, Ethics, and Humanities in Medicine , 2008, DOI: 10.1186/1747-5341-3-14
Abstract: Few drugs have been as successful blockbusters as the class of antidepressants. Cumulatively, hundreds of millions of patients have taken these medications, and the selective serotonin reuptake inhibitors (SSRIs) and newer generation drugs in particular have been immensely popular. Antidepressants reflect one of the major manifestations of medicalization of modern society [1]. In 2006, 5 of the 35 drugs with top sales in the USA were antidepressants, and each of them had sales of 1.08–2.25 billion dollars in that year (Table 1) [2]. About 30% of the cost of depression in the USA (80 billion dollars per year) goes to drug expenditures [3].This is not an epidemic that lacks evidence-based material to support it. Few drugs have had such a long chain of double-blind, placebo-controlled trials performed to demonstrate their effectiveness and to pass through seemingly strict regulatory approvals. The randomized literature of antidepressants is apparently one of the richest in evidence-based credentials. While for a large proportion of medical interventions, we have no or few clinical trials ever conducted, for antidepressants there are probably well over a thousand. PsiTri, an online library of clinical trials for mental health conditions [4], lists 4058 clinical trials for depression, and a large share of them (over a quarter of the total, exact count depends on eligibility criteria) pertain to randomized trials of antidepressants. A systematic review of SSRI trials for diverse indications until 2003 [5] found 702 trials (411 comparisons between SSRIs and placebo, 220 comparisons between SSRIs and tricyclic antidepressants, and 159 comparisons between SSRIs and active therapies other than placebos or tricyclic antidepressants). In another review [6] of 12 antidepressants where only double-blind, placebo-controlled trials for diverse indications in adults were involved, sponsors furnished data to the FDA on 406 trials with approximately 100,000 randomized patients.Formall
Why most published research findings are false.
Ioannidis John P A
PLOS Medicine , 2005,
Abstract: 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.
Author's Reply.
Ioannidis John P A
PLOS Medicine , 2005,
Abstract:
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