He holds four academic appointments atStanford University: Professor of Medicine, Professor of Epidemiology and Population Health, Professor (by courtesy) of Statistics and Professor (by courtesy) of Biomedical Data Science.[10][5] He is director of the Stanford Prevention Research Center, and co-director, along withSteven N. Goodman, of theMeta-Research Innovation Center at Stanford.[13][14]
Ioannidis's research at Stanford focuses onmeta-analysis andmeta-research – the study of studies.[29] Thomas Trikalinos and Ioannidis coined the termProteus phenomenon to describe tendency for early studies on a subject to find larger effect than later ones.[30]
He was an early and influential public critic ofTheranos, the now-fallen Silicon Valley blood test startup that at its height was valued at up to $9 billion. He criticized it for "stealth research" that it had not made available for other scientists to review.[31][32][33]
Ioannidis has defined meta-research to include "thematic areas of methods, reporting, reproducibility, evaluation, and incentives (how to do, report, verify, correct, and reward science)".[34] He has performed large-scale assessments of the presence of reproducible and transparent research indicators such asdata sharing,code sharing, protocol registration, declaration offunding and conflicts of interest inbiomedical sciences,[35]social sciences,[36] andpsychology.[37] He has led or co-led efforts to define[38] and improve reproducibility in science,[39] e.g. computational reproducibility,[40][41] and to reduce research waste in study design, conduct, and analysis.[42] Ioannidis has co-authored the Manifesto for Reproducible Science,[43] an eight-page document illuminating the need to fix the flaws in the current scientific process and mitigate the "reproducibility crisis" in science.[44]
In "Why Most Published Research Findings are False" (2005), Ioannidis focused on why most published research findings cannot be validated.[2] In a later paper onPLOS Medicine (2014), he discusses what can be done to improve this situation and make more published research findings to be true[45] and in a third paper (2016) he showed whyclinical research in particular is usually not useful and how this can be amended.[46] In the first of the three PLOS papers he stated that "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".[47] In the second paper, he discussed solutions: "adoption of large-scale collaborative research; replication culture; registration; sharing; reproducibility practices; better statistical methods; standardization of definitions and analyses; more appropriate (usually more stringent) statistical thresholds; and improvement in study design standards, peer review, reporting and dissemination of research, and training of the scientific workforce".[48][49][50] In the third paper, he proposed eight features that are important for useful clinical research: problem base, context placement, information gain, pragmatism, patient-centeredness, value for money, feasibility, and transparency.[51] Ioannidis was invited to present his findings as akeynote speaker at the"Evidence Live 2016" conference, hosted jointly by theCentre for Evidence-Based Medicine (CEBM) at theNuffield Department of Primary Care Health Sciences, University of Oxford and theBMJ.[52]
Ioannidis has developed and popularized several methods formeta-analysis and has made several conceptual advances in this field. These include methods for assessingheterogeneity and itsuncertainty,[53] methods for meta-analysis involvingmultiple treatments,[54] methods and processes forumbrella reviews,[55][56] and several approaches to identifyingbias and adjusting the results of meta-analyses for bias, such aspublication bias andreporting bias resulting infunnel-plot asymmetry.[57] He has also alerted about the misuse and misinterpretation of bias tests.[58] Along withDavid Chavalarias, he catalogued 235 biases across the entire publication record of biomedical research.[59] Ioannidis has been critical of flawed, misleading and redundant meta-analyses, estimating that few meta-analyses in medicine are both bias-free and clinically useful.[60] He has performedempirical evaluations of theconcordance of results between meta-analyses and large trials[61] and betweenrandomized trials and non-randomized studies.[62][63][64]
Ioannidis has been one of the strong proponents and earlier advocates ofevidence-based medicine. However, he has alerted that, over the years, as evidence-based medicine acquired more prominence and influence, it was hijacked to serve other agendas that are often biased.[65] In an essay written to honor his late mentorDavid Sackett, he stated that:
Influential randomized trials are largely done by and for the benefit of the industry. Meta-analyses and guidelines have become a factory, mostly also serving vested interests. National and federal research funds are funneled almost exclusively to research with little relevance to health outcomes. We have supported the growth of principal investigators who excel primarily as managers absorbing more money. Diagnosis and prognosis research and efforts to individualize treatment have fueled recurrent spurious promises. Risk factor epidemiology has excelled insalami-sliced,data-dredged articles withgift authorship and has become adept to dictating policy from spurious evidence. Under market pressure, clinical medicine has been transformed to finance-based medicine. In many places, medicine and health care are wasting societal resources and becoming a threat to human well-being.Science denialism andquacks are also flourishing and leading more people astray in their life choices, including health. Evidence-based medicine still remains an unmet goal, worthy to be attained.[66][67]
He has described four inter-related problems that create what he calls the Medical Misinformation Mess:
First, much published medical research is not reliable or is of uncertain reliability, offers no benefit to patients, or is not useful to decision makers. Second, most healthcare professionals are not aware of this problem. Third, they also lack the skills necessary to evaluate the reliability and usefulness of medical evidence. Finally, patients and families frequently lack relevant, accurate medical evidence and skilled guidance at the time of medical decision-making.[68][69]
He has supported these views by contributing to a meta-epidemiological study which found that only 1 in 20 interventions tested in Cochrane Reviews have benefits that are supported by high-quality evidence[70] and a related study showing that the quality of this evidence does not seem to improve over time.[71]
Ioannidis has contributed to several influential guidelines for reporting different types of research, such asPRISMA for meta-analyses,[79] TRIPOD for multivariableprognostic anddiagnostic models,[80] and others on clinical trials andobservational research. He is the lead author of theCONSORT for harms, a guideline that provides guidance on how to properly report on harms in randomized trials[81][82] and has contributed to PRISMA for harms, a guideline for reporting of harms in meta-analyses.[83][84]
Ioannidis was one of the first to advocate the use of meta-analysis ingenetic epidemiology to assess replication[85] and the incorporation of meta-analysis in large-scale consortia of multiple investigators performinggenome-wide association studies.[86][87] He led and contributed to many such efforts in diverse areas of genetic epidemiology and in other areas ofmolecular epidemiology.[88][87]
Ioannidis has been critical ofnutritional epidemiology research practices and has recommended reforms to improve the credibility of research in the field.[89][90] By means of empirical reviews, he has highlighted that there are studies suggesting that almost every nutrient is associated with cancer risk, which is an implausible situation[91][92] He has also suggested that more attention is needed for proper disclosures of both financial and non-financialconflicts of interest in nutrition research. He also co-authored the DIETFITS randomized trial that showed no difference between alow-fat and alow-carb diet.[93][94]
In an effort to improve the credibility of research onrisk factors, Ioannidis has proposed that exposure-wide or environment-wideassociation studies should be performed and he has outlined the similarities and differences between such studies and genome-wide association studies in genetics.[95][96] By assessing all risk factors together instead of one at a time, this practice aims to reduceselective reporting and publication bias. He has also advocated for the use of large national population databases with systematically collected data to minimize bias and improve yield of trustworthy discoveries.[97] He has worked on the potential uses of such approaches inbig data[98] andartificial intelligence.[99][100]
Ioannidis has performed critical assessments of the evidence behindmental health interventions (pharmacotherapy andpsychotherapy). He co-authored anetwork meta-analysis on more than 500 randomized trials ofanti-depressants showing a modest benefit from these medications formajor depression.[101][102][103] He has identified the potential for sponsorship bias in meta-analyses in mental health[104][105] and has empirically assessed the totality of meta-analyses on mental health interventions, estimating that beneficial effects do exist, but they tend to be modest and thus a research agenda is needed to identify more effective interventions.[106]
Along with colleagues, Ioannidis has performed empirical evaluations and meta-research assessments of large numbers of scientific studies inneuroscience and have found that lack of power is a very common problem, leading to bothfalse-negatives (the inability to discover true signals) andfalse-positives (finding spurious signals).[107][108]
In empirical assessments of all meta-analyses that have been conducted oneconomics topics, Ioannidis and colleagues have found that most of the studies in these fields are small andunder-powered. Using bias detection and correction methods, they have concluded that nearly 80% of the reported effects in the empirical economics literature is exaggerated; typically by a factor of two, and with one-third inflated by a factor of four or more.[109][110]
In an editorial onSTAT published March 17, 2020, Ioannidis wondered whether the global response to the COVID-19 pandemic may be a "once-in-a-century evidence fiasco" and asked for obtaining more reliable data to deal with the pandemic.[5] He made a rough estimation that the coronavirus could cause 10,000 U.S. deaths if it infected 1% of the U.S. population, but argued that more data was needed to determine how widely the virus would spread.[128][3][5]The virus in fact eventually became widely disseminated, and would cause more than one million deaths in the U.S.[129][128][3] Ioannidis expressed doubt that vaccines or treatments would be developed and tested in time to affect how the pandemic would unfold.[130]Marc Lipsitch, Director of the Center for Communicable Disease Dynamics at theHarvard T.H. Chan School of Public Health, objected to Ioannidis's characterization of the global response in a reply that was published on STAT the next day after Ioannidis's.[131]
In March 2020, Ioannidis tried to organize a meeting at the White House where he and colleagues would caution PresidentDonald Trump against "shutting down the country for [a] very long time and jeopardizing so many lives in doing this", according to a proposal he submitted. The meeting did not come to pass, but on March 28, after Trump said he wanted the country reopened by Easter, Ioannidis wrote to his colleagues, "I think our ideas have inflitrated [sic] the White House regardless".[3]
Ioannidis widely promoted a study of which he had been co-author, "COVID-19AntibodySeroprevalence in Santa Clara County, California", released as apreprint on April 17, 2020. It asserted thatSanta Clara County's number of infections was between 50 and 85 times higher than the official count, putting the virus's fatality rate as low as 0.1% to 0.2%.[n 1][133][129] Ioannidis concluded from the study that the coronavirus is "not the apocalyptic problem we thought".[134] The message found favor with right-wing media outlets, but the paper drew criticism from a number of epidemiologists who said its testing was inaccurate and its methods were sloppy.[135][136][137] Writing forWired,David H. Freedman said that the Santa Clara study compromised Ioannidis's previously excellent reputation and meant that future generations of scientists may remember him as "the fringe scientist who pumped up a bad study that supported a crazy right-wing conspiracy theory in the middle of a massive health crisis".[4] Ioannidis has also promoted the idea that there were financial incentives to put COVID-19 on death certificates and as such, they were unreliable during the pandemic, as well as the idea that doctors killed COVID-19 patients through premature intubations. Both of these beliefs contradict the available evidence.[138]
It was later reported that the study received $5,000 in funding fromthe founder of theJetBlue airline, which led to criticism over a potential conflict of interest.[139][140] In a guest opinion article inScientific American, former colleagues of Ioannidis wrote that a legal firm had determined he had no financial conflict.[141] A review by the Stanford School of Medicine faulted the study for shortcomings including a public perception of a conflict of interest, but found "no evidence that any of the study funders influenced the design, execution, or reporting of the study".[128]
Amid controversy over his COVID-19 work and his frequent televised interviews, Ioannidis was harassed in memes and emails, including one falsely claiming his mother died of COVID-19. Some scientists and commentators voiced concerns over the backlash and the highly politicized scientific dispute in general.[128][142]
In February 2022 Ioannidis co-authored a paper examining the role of indoor and outdoorair quality in the spread ofSARS-CoV-2, which concluded thatenvironmental health may be a crucial component in the prevention of COVID-19 and suggested preventive measures such as indoorCO2 monitoring andmechanical ventilation.[144]
In 2022, Ioannidis authored a paper inBMJ Open arguing that signatories of theGreat Barrington Declaration were shunned as a fringe minority by those in favor of theJohn Snow Memorandum. According to him, the latter used their large numbers of followers onTwitter and othersocial media andop-eds to shape a scientificgroupthink against the former, who had less influence as measured by theKardashian Index.[145][146]The BMJ published responses to his paper, including a comment byGavin Yamey,David Gorski, and Gideon Meyerowitz-Katz which argued that Ioannidis's paper featured "factual errors, statistical shortcomings, failure to protect the named research subjects from harm, and potentially undeclared conflicts of interest that entirely undermine the analysis presented".[147] In the same exchange of comments onThe BMJ, Ioannidis addressed the concerns of Yamey, Gorski and Meyerovitz-Katz in his "Fourth set of replies", additionally stating that his "COVID-19 papers have been cited about 5 thousand times in the scientific literature by tens of thousands of scientists and were discussed by millions of people," and dismissed conflict of interest by asserting that he did not sign the Great Barrington Declaration or any other petition or signature collection on COVID-19, as he is against the notion that scientific matters and evidence could be decided by signature collections and prefers these matters be handled by heavily moderated public debates.[148]
In the 2000s and 2010s, during a period of regular publications from Ioannidis on thereplication crisis in science, observers in the popular press commented that Ioannidis "may be one of the most influential scientists alive",[149][150] and was "cementing his role as one of medicine's top mythbusters".[151]
This acclaim continued into the late 2010s, withWired mentionining Ioannidis as "arguably the replication crisis' chief inquisitor".[110] His research on replicability reached multiple fields, including the specious statistics behind some drug subscriptions,[64] and findings from Ioannidis that only a minority of widely cited health research studies carried out over the last decade could be replicated, with at least 1 in 6 actually being contradicted by later studies,.[154]Elsevier featured his analogy of reproducibility in research to "taming a complex beast".[155] He was also an early critic ofTheranos.[156]
However, Ioannidis's popularity began to wane during the COVID-19 pandemic, with some peers and colleagues criticizing his rhetoric and seeming loss of objectivity compared to his prior work.[157] A March 2020 editorial in STAT news was particularly criticized, where he predicted the pandemic would result in 10,000 deaths at most.[158] In 2021,David Gorski's article "What the heck happened to John Ioannidis?" described statements by Ioannidis about COVID-19 as inflammatory and politically charged, and said Ioannidis had made egregiousad hominem attacks. Gorski called Ioannidis "a cautionary tale of how even science watchdogs can fall prey to hubris".[129] Ioannidis later denied that he mocked other researchers who expressed concern about the death toll of the pandemic.[159]
^On May 11, the study's authors revised the study with new figures stating the number of infections was 54 times higher than the official count.[132][129]
^Antiochou, Konstantina; Psillos, Stathis (2022), Oswald, Steve; Lewiński, Marcin; Greco, Sara; Villata, Serena (eds.), "How to Handle Reasonable Scientific Disagreement: The Case of COVID-19",The Pandemic of Argumentation, vol. 43, Cham: Springer International Publishing, pp. 65–83,doi:10.1007/978-3-030-91017-4_4,ISBN978-3-030-91016-7{{citation}}: CS1 maint: work parameter with ISBN (link)
^"John P.A. Ioannidis". Department of Hygiene and Epidemiology, University of Ioannina School of Medicine.Archived from the original on February 14, 2009. RetrievedDecember 31, 2008.
^Ioannidis, John P. A.; Ntzani, Evangelia E.; Trikalinos, Thomas A.; Contopoulos-Ioannidis, Despina G. (November 1, 2001). "Replication validity of genetic association studies".Nature Genetics.29 (3):306–309.doi:10.1038/ng749.ISSN1061-4036.PMID11600885.S2CID6742347.
^Ioannidis, John P. A. (February 17, 2015). "Stealth Research: Is Biomedical Innovation Happening Outside the Peer-Reviewed Literature?".JAMA.313 (7):663–664.doi:10.1001/jama.2014.17662.ISSN0098-7484.PMID25688775.
^Salanti, Georgia; Ades, A. E.; Ioannidis, John P. A. (February 2011). "Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial".Journal of Clinical Epidemiology.64 (2):163–171.doi:10.1016/j.jclinepi.2010.03.016.ISSN1878-5921.PMID20688472.
^Bellou, Vanesa; Belbasis, Lazaros; Tzoulaki, Ioanna; Evangelou, Evangelos; Ioannidis, John P. A. (February 2016). "Environmental risk factors and Parkinson's disease: An umbrella review of meta-analyses".Parkinsonism & Related Disorders.23:1–9.doi:10.1016/j.parkreldis.2015.12.008.hdl:10044/1/31820.ISSN1873-5126.PMID26739246.
^Cappelleri, J. C.; Ioannidis, J. P.; Schmid, C. H.; de Ferranti, S. D.; Aubert, M.; Chalmers, T. C.; Lau, J. (October 23–30, 1996). "Large trials vs meta-analysis of smaller trials: how do their results compare?".JAMA.276 (16):1332–1338.doi:10.1001/jama.1996.03540160054033.ISSN0098-7484.PMID8861993.
^Ioannidis, J. P.; Haidich, A. B.; Pappa, M.; Pantazis, N.; Kokori, S. I.; Tektonidou, M. G.; Contopoulos-Ioannidis, D. G.; Lau, J. (August 15, 2001). "Comparison of evidence of treatment effects in randomized and nonrandomized studies".JAMA.286 (7):821–830.doi:10.1001/jama.286.7.821.ISSN0098-7484.PMID11497536.
^Moons, Karel G. M.; Altman, Douglas G.; Reitsma, Johannes B.; Ioannidis, John P. A.; Macaskill, Petra; Steyerberg, Ewout W.; Vickers, Andrew J.; Ransohoff, David F.; Collins, Gary S. (January 6, 2015). "Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD): explanation and elaboration".Annals of Internal Medicine.162 (1): W1–73.doi:10.7326/M14-0698.hdl:1874/333472.ISSN1539-3704.PMID25560730.S2CID24729487.
^Ioannidis, John P. A.; Evans, Stephen J. W.; Gøtzsche, Peter C.; O'Neill, Robert T.; Altman, Douglas G.; Schulz, Kenneth; Moher, David; CONSORT Group (November 16, 2004). "Better reporting of harms in randomized trials: an extension of the CONSORT statement".Annals of Internal Medicine.141 (10):781–788.doi:10.7326/0003-4819-141-10-200411160-00009.ISSN1539-3704.PMID15545678.S2CID17032571.
^"PRISMA".prisma-statement.org.Archived from the original on March 19, 2022. RetrievedMay 12, 2022.
^Ioannidis, J. P.; Ntzani, E. E.; Trikalinos, T. A.; Contopoulos-Ioannidis, D. G. (November 2001). "Replication validity of genetic association studies".Nature Genetics.29 (3):306–309.doi:10.1038/ng749.ISSN1061-4036.PMID11600885.S2CID6742347.
^abHolden (June 6, 2013)."Meta-research update".The GiveWell Blog.Archived from the original on May 17, 2021. RetrievedMay 12, 2022.
^Ioannidis, John P. A.; Gwinn, Marta; Little, Julian; Higgins, Julian P. T.; Bernstein, Jonine L.; Boffetta, Paolo; Bondy, Melissa; Bray, Molly S.; Brenchley, Paul E.; Buffler, Patricia A.; Casas, Juan Pablo (January 2006). "A road map for efficient and reliable human genome epidemiology".Nature Genetics.38 (1):3–5.doi:10.1038/ng0106-3.ISSN1061-4036.PMID16468121.S2CID23985692.
^Schoenfeld, Jonathan D; Ioannidis, John PA (2013). "Is everything we eat associated with cancer? A systematic cookbook review".The American Journal of Clinical Nutrition.97 (1):127–134.doi:10.3945/ajcn.112.047142.PMID23193004.
^Hsing, Ann W.; Ioannidis, John P. A. (September 2015). "Nationwide Population Science: Lessons From the Taiwan National Health Insurance Research Database".JAMA Internal Medicine.175 (9):1527–1529.doi:10.1001/jamainternmed.2015.3540.ISSN2168-6114.PMID26192815.
^Brownlee, Jeanne Lenzer, Shannon."The COVID Science Wars".Scientific American.Archived from the original on December 1, 2020. RetrievedDecember 1, 2020.{{cite web}}: CS1 maint: multiple names: authors list (link)
^Max-Delbrück-Centrum, Berliner Institut für Gesundheitsforschung-Charité und."Visiting Fellows - BIH at Charité".www.bihealth.org.Archived from the original on February 3, 2022. RetrievedFebruary 2, 2022.
^"Novim 2018 Awards"(PDF). May 7, 2018.Archived(PDF) from the original on February 3, 2022. RetrievedFebruary 3, 2022.
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