background preloader

Why Most Published Research Findings Are False

Why Most Published Research Findings Are False
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. Figures Citation: Ioannidis JPA (2005) Why Most Published Research Findings Are False. Published: August 30, 2005 Copyright: © 2005 John P. Abbreviation: PPV, positive predictive value Bias Related:  Medical Research & EBMFrau científic

genome browser 72: Homo sapiens - Description Human Login/Register More ▼ Search Human Search all species Search Ensembl genomes Search Vega Search EMBL-EBI Search Sanger Favourite species All species Recent locations Clear history Recent jobs Human Homo sapiens What's New in Human release 83 More news... View karyotype Example region Genome assembly: GRCh38.p5 (GCA_000001405.20) More information and statistics Download DNA sequence (FASTA) Convert your data to GRCh38 coordinates Display your data in Ensembl Other assemblies Example gene Example transcript Gene annotation What can I find? More about this genebuild, including RNASeq gene expression models Download genes, cDNAs, ncRNA, proteins (FASTA) Update your old Ensembl IDs Additional manual annotation can be found in Vega Example gene tree Comparative genomics What can I find? More about comparative analysis Download alignments (EMF) Example variant Example phenotype Example structural variant Variation What can I find? More about variation in Ensembl Download all variants (GVF) Variant Effect Predictor Regulation Blog

Dos investigadores cometieron el 25% de los fraudes científicos Un estudio trata de explicar por qué el fraude científico se ha multiplicado por diez desde 1975 Más noticias de: fraude, política científica En 1936, Ronald Fisher, uno de los padres de la ciencia estadística moderna, publicó un artículo de 26 páginas en el que criticaba otro trabajo científico de 1866 escrito por un monje austriaco titulado Versuche über Pflanzenhybriden. Ampliar Sin embargo, algunos análisis recientes indican que se está produciendo un incremento preocupante en la anulación de artículos científicos por distintos tipos de fraude y mala práctica. Ahora, un grupo de investigadores —entre los que se encuentran los autores del artículo que señalaba el incremento de la retirada de trabajos científicos de las revistas en PNAS— ha tratado de dar alguna explicación a este fenómeno. Al menos un 2% de los investigadores habrían cometido fraude al menos alguna vez en su carrera, según un estudio Varias explicaciones

Why Steve Jobs Matters to You - Bill Taylor by Bill Taylor | 10:31 AM August 30, 2011 Editor’s note: This post was written after Steve Jobs’ resignation in August; upon the news of his death, we think it’s worth another read. All sorts of commentators, on this site and elsewhere, are asking all sorts of questions about the resignation of Steve Jobs as CEO of Apple, Inc. What does it mean for the company’s future? What does it means for the stock price? All fine questions, to which I would add one more: What does it mean for you? Few of us have the chance to achieve 1/100th of what Steve Jobs has achieved. So if you want to use the end of Steve Jobs’s hands-on leadership at Apple to inspire a greater commitment to leadership by you, I’d suggest that you ask these five simple questions — questions that define what it means to be a high-impact leader today. 1. 2. 3. 4. 5. You don’t have to aspire to be the next Steve Jobs to learn some lessons from his one-of-a-kind career.

History, Policies, and Laws - ClinicalTrials.gov This page provides information on selected events, policies, and laws related to the development and expansion of ClinicalTrials.gov. It is not intended to be comprehensive. Contents 1997: Congress Passes Law (FDAMA) Requiring Trial Registration The first U.S. Section 113 of FDAMA required that the National Institutes of Health (NIH) create a public information resource on certain clinical trials regulated by the Food and Drug Administration (FDA). The information in the registry was intended for a wide audience, including individuals with serious or life-threatening diseases or conditions, members of the public, health care providers, and researchers. 2000: NIH Releases ClinicalTrials.gov Web Site With input from FDA and others, the NIH National Library of Medicine (NLM) developed ClinicalTrials.gov. NIH Press Release: National Institutes of Health Launches ClinicalTrials.gov (February 29, 2000) 2000–2004: FDA Issues Guidance for Industry Documents This page last reviewed in February 2015

The Human Protein Atlas Antes se pilla a un científico mentiroso que a un estadístico cojo | mvadillo.com Dirk Smeesters y Lawrence Sanna protagonizaron dos de los casos más sonados de fraude científico del pasado 2012. En un breve artículo que acaba de publicarse en Psychological Science, Uri Simonsohn nos revela cómo descubrió que estos dos autores se habían inventado datos, todo ello sin recurrir más que a un poco de estadística elemental y a una gran dosis de ingenio. Se trata en ambos casos de experimentos sobre el llamado priming social, un misterioso efecto investigado por psicólogos sociales según el cual comportamientos tan complejos como la conducta altruista o incluso el rendimiento en un test de cultura general pueden verse influidos por estímulos sutiles de cuyo efecto apenas somos conscientes (sic). El segundo caso es una investigación similar de los datos de varios experimentos de Smeesters. Indagando en los datos sobre otros estudios del mismo autor, Simonsohn descubrió más irregularidades de este tipo, algunas de ellas francamente ingeniosas. Simonsohn, U. (2012).

How Great Companies Think Differently Idea in Brief Traditional theories of the firm are dominated by the notion of opposition between capital and labor, disconnecting business from society and posing conflicts between them. According to this view, companies are nothing more than money-generating machines. By contrast, great companies use a different operating logic. Great companies work to make money, but in their choices of how to do so, they consider whether they are building enduring institutions. There are six facets of institutional logic, which radically alters leadership and corporate behavior: a common purpose; a long-term view; emotional engagement; community building; innovation; and self-organization. Artwork: Sarah Morris, Midtown—HBO/Grace, 1999, Gloss household paint on canvas, 213.4 × 213.4 cm It’s time that beliefs and theories about business catch up with the way great companies operate and how they see their role in the world today.

Drug companies aren’t telling you the whole truth Six years ago, I wrote a story for Bloomberg News about an interesting research review that looked at which studies of antidepressants such as Prozac, Paxil and Zoloft got published in medical journals and which didn’t. The review found that almost every clinical trial that got published in a medical journal—a whopping 94 percent of them—had positive findings, meaning they showed the drugs worked. Those were the studies that got published; those were the studies that doctors and patients could turn to for guidance. But did they accurately represent the conclusions of all the studies of a particular drug that had been conducted? Hardly. The psychiatrist who led the review, a former FDA medical officer named Erick Turner, knew about another source of information on completed studies: the application packet that drugmakers submit when they seek to get a medication approved. The study caused a bit of a stir. “There is still a problem here,” Peterson says.

MalaCards - human disease database Se multiplican por 10 los fraudes científicos en los últimos 40 años MADRID, 26 Sep. (EUROPA PRESS) - En los últimos 40 años se ha multiplicado el número de trabajos de investigación publicados, pero también lo ha hecho, y por 10, el problema del fraude científico, según han alertado este jueves expertos reunidos en la jornada 'El fraude científico, a examen', organizada por la Fundación de Ciencias de la Saluden, en colaboración con GlaxoSmithKline y la Fundación Rafael del Pino. Incentivar la buena investigación y mantener los estándares de calidad y ética son los mejores instrumentos para detener el avance de los fraudes. Según el doctor Bouza, "hay que despresurizar la amenaza a los científicos sobre que una producción debe de ser de determinada magnitud y realizarse a una determinada velocidad. El progreso científico se sustenta en dos firmes creencias, según explica el director del CIC bioGUNE y patrono de la Fundación de Ciencias de la Salud, el profesor José María Mato.

human disease database MalaCards is an integrated searchable database of human maladies and their annotations, modeled on the architecture and richness of the popular GeneCards database of human genes. MalaCards leverages GeneCards and GeneDecks and their associated genes. Each "card" contains a variety of detailed sections. For example, see our Sample Malady. MalaCards search guide This page provides information about the various MalaCards sections and tables. MalaCards Disease List An offline process is responsible for generating the comprehensive integrated list of diseases by mining heterogeneous, partially overlapping sources (see below for list of sources), unifying names and acronyms, and organizing characterizations. Disease name unification is effected by transforming each name to a canonical form. For each malady a unique symbol is generated, composed of the first letter of its name, followed by the next two consonants, followed by a serial number. MalaCards Header Annotation schemes MalaCards Scores Genes

Scientific misconduct Scientific misconduct is the violation of the standard codes of scholarly conduct and ethical behavior in professional scientific research. A Lancet review on Handling of Scientific Misconduct in Scandinavian countries provides the following sample definitions:[1] (reproduced in The COPE report 1999.)[2] Danish definition: "Intention or gross negligence leading to fabrication of the scientific message or a false credit or emphasis given to a scientist"Swedish definition: "Intention[al] distortion of the research process by fabrication of data, text, hypothesis, or methods from another researcher's manuscript form or publication; or distortion of the research process in other ways." The consequences of scientific misconduct can be damaging for both perpetrators[3][4] and any individual who exposes it.[5] In addition there are public health implications attached to the promotion of medical or other interventions based on dubious research findings. Career pressure Ease of fabrication

Retraction Watch | Tracking retractions as a window into the scientific process

Related: