Unesco sobre el Covid-19: directrices éticas para una respuesta global / Unesco on covid-19: ethical guidelines for a global response

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A tutorial in assessing disclosure risk in microdata.

Statistical agencies are releasing statistical data to other agencies for research purposes or to inform public policy. Prior to data release, these agencies have a legal and ethical obligation to protect the confidentiality of individuals in the data. Agencies often release altered versions of the data, but there usually remains risks of disclosure. Many well-studied risk measures are available to assess risk; however, many agencies today continue to use subjective judgement, past experience, and ad hoc rules or checklists to assess disclosure risk. More recently, there has been a recognized demand for quantitative risk measures that provide a more objective criteria for data release. This tutorial provides an overview of the statistical disclosure control framework for microdata. We focus on the risk analysis stage within this framework by defining existing disclosure risk measures and how to estimate them with available software.

P-hacking.

Due to increasing rate of research articles getting published in recent times, medical science is evolving very fast. Open access policies of the journals make information easily available to the stakeholders for building future research proposal. Blindly having faith in whatever is published in the literature is detrimental for science; however, it is hard to find out the correctness of the data analysis.

Research Ethics in Big Data

The ethical conduct of research includes, in part, patient agreement to participate in studies and the protection of health information. In the evolving world of data science and the accessibility of large quantities of web-based data created by millions of individuals, novel methodologic approaches to answering research questions are emerging. This article explores research ethics in the context of big data.

P-Hacking in Orthopaedic Literature: A Twist to the Tail.

BACKGROUND: "P-hacking" occurs when researchers preferentially select data or statistical analyses until nonsignificant results become significant. We wanted to evaluate if the phenomenon of p-hacking was evident in orthopaedic literature. METHODS: We text-mined through all articles published in three top orthopaedic journals in 2015. For anonymity, we cipher-coded the three journals. We included all studies that reported a single p value to answer their main hypothesis. These p values were then charted and frequency graphs were generated to illustrate any evidence of p-hacking. Binomial tests were employed to look for evidence of evidential value and significance of p-hacking. RESULTS: Frequency plots for all three journals revealed evidence of p-hacking. Binomial tests for all three journals were significant for evidence of evidential value (p < 0.0001 for all). However, the binomial test for p-hacking was significant only for one journal (p = 0.0092). CONCLUSIONS: P-hacking is an evolving phenomenon that threatens to jeopardize the evidence-based practice of medicine. Although our results show that there is good evidential value for orthopaedic literature published in our top journals, there is some evidence of p-hacking of which authors and readers should be wary.

Playing with Data--Or How to Discourage Questionable Research Practices and Stimulate Researchers to Do Things Right.

Recent fraud cases in psychological and medical research have emphasized the need to pay attention to Questionable Research Practices (QRPs). Deliberate or not, QRPs usually have a deteriorating effect on the quality and the credibility of research results. QRPs must be revealed but prevention of QRPs is more important than detection. I suggest two policy measures that I expect to be effective in improving the quality of psychological research. First, the research data and the research materials should be made publicly available so as to allow verification. Second, researchers should more readily consider consulting a methodologist or a statistician. These two measures are simple but run against common practice to keep data to oneself and overestimate one's methodological and statistical skills, thus allowing secrecy and errors to enter research practice.

The Ethics of Big Data: Current and Foreseeable Issues in Biomedical Contexts.

The capacity to collect and analyse data is growing exponentially. Referred to as 'Big Data', this scientific, social and technological trend has helped create destabilising amounts of information, which can challenge accepted social and ethical norms. Big Data remains a fuzzy idea, emerging across social, scientific, and business contexts sometimes seemingly related only by the gigantic size of the datasets being considered. As is often the case with the cutting edge of scientific and technological progress, understanding of the ethical implications of Big Data lags behind. In order to bridge such a gap, this article systematically and comprehensively analyses academic literature concerning the ethical implications of Big Data, providing a watershed for future ethical investigations and regulations. Particular attention is paid to biomedical Big Data due to the inherent sensitivity of medical information. By means of a meta-analysis of the literature, a thematic narrative is provided to guide ethicists, data scientists, regulators and other stakeholders through what is already known or hypothesised about the ethical risks of this emerging and innovative phenomenon. Five key areas of concern are identified: (1) informed consent, (2) privacy (including anonymisation and data protection), (3) ownership, (4) epistemology and objectivity, and (5) 'Big Data Divides' created between those who have or lack the necessary resources to analyse increasingly large datasets. Critical gaps in the treatment of these themes are identified with suggestions for future research. Six additional areas of concern are then suggested which, although related have not yet attracted extensive debate in the existing literature. It is argued that they will require much closer scrutiny in the immediate future: (6) the dangers of ignoring group-level ethical harms; (7) the importance of epistemology in assessing the ethics of Big Data; (8) the changing nature of fiduciary relationships that become increasingly data saturated; (9) the need to distinguish between 'academic' and 'commercial' Big Data practices in terms of potential harm to data subjects; (10) future problems with ownership of intellectual property generated from analysis of aggregated datasets; and (11) the difficulty of providing meaningful access rights to individual data subjects that lack necessary resources. Considered together, these eleven themes provide a thorough critical framework to guide ethical assessment and governance of emerging Big Data practices.

Gestión de los datos brutos de investigación en los investigadores españoles en ciencias de la salud / Research raw data management in Spanish researchers on health sciences

Objetivo: Identificar los hábitos y experiencias actuales de los investigadores españoles en ciencias de la salud en relación con la gestión y el intercambio de los datos brutos de investigación. Material y métodos: Se diseñó un cuestionario compuesto por 40 preguntas distribuidas en tres bloques: A) Datos personales; B) Creación y reutilización de los datos; C) Preservación de los datos. El cuestionario se envió por correo electrónico a investigadores de varias instituciones sanitarias españolas. Resultados: La mayor parte de los investigadores españoles de las ciencias de la salud carecen de políticas o planes de gestión de los datos. Los principales motivos por los que no existen estas políticas son la ausencia de una obligación institucional, la falta de tiempo, el esfuerzo que se requiere para llevarlas a cabo y la falta de apoyo y de orientación. Desconocen la existencia de repositorios, instituciones y otros sistemas de gestión y preservación de los datos. A una amplia mayoría les gustaría utilizar los datos de investigación recogidos por otros investigadores. Conclusión: Es necesario adquirir una conciencia colectiva de la importancia de compartir datos como práctica que mejora la transmisión del conocimiento y, en definitiva, la eficiencia de las inversiones en investigación. También es necesario que las agencias e instituciones que financian la investigación proporcionen el apoyo necesario para llevarla a cabo (AU)

Objective: To identify the habits and current experiences of Spanish researchers in health sciences in relation to the management and sharing of raw research data. Material and method: A questionnaire composed of 40 questions divided into three blocks was designed: A) Personal information; B) Creation and reuse of data; C) Preservation of Data. The questionnaire was sent by email to researchers from several Spanish healthcare institutions. Results: Most of the Spanish health science researchers lack policies or plans of management data. The main reasons why there are no such policies is the absence of an institutional duty, lack of time, the effort required to carry it out and lack of support and guidance. They are unaware of repositories, institutions and other systems of management and data preservation. A vast majority would like to use the research data collected by other researchers. Conclusion: It is necessary to acquire a collective awareness of the importance of data sharing as a practice that improves the transmission of knowledge and, ultimately, the efficiency of research investments. It is also required that the agencies and institutions that fund research provide the necessary support to carry it out (AU)

Using Ethical Reasoning to Amplify the Reach and Resonance of Professional Codes of Conduct in Training Big Data Scientists.

The use of Big Data--however the term is defined--involves a wide array of issues and stakeholders, thereby increasing numbers of complex decisions around issues including data acquisition, use, and sharing. Big Data is becoming a significant component of practice in an ever-increasing range of disciplines; however, since it is not a coherent "discipline" itself, specific codes of conduct for Big Data users and researchers do not exist. While many institutions have created, or will create, training opportunities (e.g., degree programs, workshops) to prepare people to work in and around Big Data, insufficient time, space, and thought have been dedicated to training these people to engage with the ethical, legal, and social issues in this new domain. Since Big Data practitioners come from, and work in, diverse contexts, neither a relevant professional code of conduct nor specific formal ethics training are likely to be readily available. This normative paper describes an approach to conceptualizing ethical reasoning and integrating it into training for Big Data use and research. Our approach is based on a published framework that emphasizes ethical reasoning rather than topical knowledge. We describe the formation of professional community norms from two key disciplines that contribute to the emergent field of Big Data: computer science and statistics. Historical analogies from these professions suggest strategies for introducing trainees and orienting practitioners both to ethical reasoning and to a code of professional conduct itself. We include two semester course syllabi to strengthen our thesis that codes of conduct (including and beyond those we describe) can be harnessed to support the development of ethical reasoning in, and a sense of professional identity among, Big Data practitioners.

Outsourcing medical data analyses: can technology overcome legal, privacy, and confidentiality issues?

BACKGROUND: Medical data are gold mines for deriving the knowledge that could change the course of a single patient's life or even the health of the entire population. A data analyst needs to have full access to relevant data, but full access may be denied by privacy and confidentiality of medical data legal regulations, especially when the data analyst is not affiliated with the data owner. OBJECTIVE: Our first objective was to analyze the privacy and confidentiality issues and the associated regulations pertaining to medical data, and to identify technologies to properly address these issues. Our second objective was to develop a procedure to protect medical data in such a way that the outsourced analyst would be capable of doing analyses on protected data and the results would be comparable, if not the same, as if they had been done on the original data. Specifically, our hypothesis was there would not be a difference between the outsourced decision trees built on encrypted data and the ones built on original data. METHODS: Using formal definitions, we developed an algorithm to protect medical data for outsourced analyses. The algorithm was applied to publicly available datasets (N=30) from the medical and life sciences fields. The analyses were performed on the original and the protected datasets and the results of the analyses were compared. Bootstrapped paired t tests for 2 dependent samples were used to test whether the mean differences in size, number of leaves, and the accuracy of the original and the encrypted decision trees were significantly different. RESULTS: The decision trees built on encrypted data were virtually the same as those built on original data. Out of 30 datasets, 100% of the trees had identical accuracy. The size of a tree and the number of leaves was different only once (1/30, 3%, P=.19). CONCLUSIONS: The proposed algorithm encrypts a file with plain text medical data into an encrypted file with the data protected in such a way that external data analyses are still possible. The results show that the results of analyses on original and on protected data are identical or comparably similar. The approach addresses the privacy and confidentiality issues that arise with medical data and is adherent to strict legal rules in the United States and Europe regarding the processing of the medical data.

Sample size requirements for interval estimation of the strength of association effect sizes in multiple regression analysis / Requisitos del tamaño de la muestra para la estimación por intervalo de la fuerza de asociación de tamaños de efecto en análisis de regresión

Antecedentes: la práctica al presentar e interpretar el tamaño de efecto ha sido recomendada extensivamente en revistas académicas al analizar resultados primarios en estudios empíricos. En consecuencia, el coeficiente de correlación múltiple al cuadrado de la muestra es el índice de fuerzas de asociación que se presente con más frecuencia en aplicaciones prácticas de regresión lineal múltiple. Método: este trabajo examina el procedimiento del tamaño de la muestra que Bonett y Wright propusieron para una precisa estimación por intervalos de coeficiente de correlación múltiple al cuadrado. Resultados: el resultado de esta simulación señala que su método simple para alcanzar la deseada precisión de la amplitud esperada proporciona el resultado satisfactorio solamente cuando el tamaño de la muestra sea extensivo. Además, la fórmula del tamaño de la muestra sugerida para lograr la designada probabilidad garantizada es inexacta y problemática. Conclusiones: de acuerdo con estos descubrimientos, no se recomienda el procedimiento del tamaño de la muestra (AU)

Background: Effect size reporting and interpreting practices have been extensively recommended in academic journals when analyzing primary outcomes of all empirical studies. Accordingly, the sample squared multiple correlation coefficient is the commonly reported strength of association index in practical applications of multiple linear regression. Method: This paper examines the sample size procedures proposed by Bonett and Wright for precise interval estimation of the squared multiple correlation coefficient. Results: The simulation results showed that their simple method for attaining the desired precision of expected width provides satisfactory results only when sample sizes are large. Moreover, the suggested sample size formula for achieving the designated assurance probability is inaccurate and problematic. Conclusions: According to these findings, their sample size procedures are not recommended (AU)

Karl Pearson and eugenics: personal opinions and scientific rigor.

The influence of personal opinions and biases on scientific conclusions is a threat to the advancement of knowledge. Expertise and experience does not render one immune to this temptation. In this work, one of the founding fathers of statistics, Karl Pearson, is used as an illustration of how even the most talented among us can produce misleading results when inferences are made without caution or reference to potential bias and other analysis limitations. A study performed by Pearson on British Jewish schoolchildren is examined in light of ethical and professional statistical practice. The methodology used and inferences made by Pearson and his coauthor are sometimes questionable and offer insight into how Pearson's support of eugenics and his own British nationalism could have potentially influenced his often careless and far-fetched inferences. A short background into Pearson's work and beliefs is provided, along with an in-depth examination of the authors' overall experimental design and statistical practices. In addition, portions of the study regarding intelligence and tuberculosis are discussed in more detail, along with historical reactions to their work.

The potential for bias in reporting of industry-sponsored clinical trials.

Concerns about potentially misleading reporting of pharmaceutical industry research have surfaced many times. The potential for duality (and thereby conflict) of interest is only too clear when you consider the sums of money required for the discovery, development and commercialization of new medicines. As the ability of major, mid-size and small pharmaceutical companies to innovate has waned, as evidenced by the seemingly relentless decline in the numbers of new medicines approved by Food and Drug Administration and European Medicines Agency year-on-year, not only has the cost per new approved medicine risen: so too has the public and media concern about the extent to which the pharmaceutical industry is open and honest about the efficacy, safety and quality of the drugs we manufacture and sell. In 2005 an Editorial in Journal of the American Medical Association made clear that, so great was their concern about misleading reporting of industry-sponsored studies, henceforth no article would be published that was not also guaranteed by independent statistical analysis. We examine the precursors to this Editorial, as well as its immediate and lasting effects for statisticians, for the manner in which statistical analysis is carried out, and for the industry more generally.

Does participation in an alcohol administration study increase risk for excessive drinking?

It has long been thought that research protocols involving alcohol administration may exacerbate problem drinking in alcoholic subjects following their participation in such a study. However, recent studies suggest that involvement in an alcohol administration study does not, in fact, have a negative impact on subsequent drinking behavior. In the present study, 27 non-treatment-seeking alcohol-dependent subjects and 32 social drinkers participated in an alcohol administration study designed to investigate the effects of repeated doses of alcohol on craving, mood, and alcohol-seeking behavior. The volume of alcohol administered to the subjects was calculated in such a way that their blood alcohol concentration would reach a peak of 0.08 g/dl midway through testing. Before their release, alcohol-dependent subjects were given feedback regarding their level of alcohol consumption and provided with information about the potential harmful effects of their drinking behavior. Percentage of days abstinent (PDA), drinks per drinking day (D/DD), and percentage of heavy drinking days (PHDD, defined as >or=4 drinks per occasion for females and >or=5 drinks per occasion for males) were recorded for the 6 weeks preceding laboratory testing and for the 6 weeks following participation in the study. The alcohol-dependent subjects exhibited a significant increase of 24% in PDA during the poststudy period compared to the prestudy period. They also decreased their D/DD by 2.4 drinks per occasion, and decreased their PHDD by 21.6%. There were no differences in PDA or D/DD for the social drinkers between pre- and poststudy periods. There was, however, a small but significant increase of 3.5% in PHDD for the social drinkers following laboratory testing. These data suggest that participation in an alcohol administration study does not put alcoholic subjects at risk for increased alcohol consumption following study participation. In fact, participation in such studies may actually precipitate at least a temporary decrease in alcohol consumption, especially when paired with a brief intervention session. Thus, non-treatment-seeking alcoholics can be safely included in alcohol administration studies to provide results that are most relevant to the population of interest.