(2013). Informed Consent, Big Data, and the Oxymoron of Research That Is Not Research. The American Journal of Bioethics: Vol. 13, No. 4, pp. 40-42. doi: 10.1080/15265161.2013.768864.

Biomedical innovation and translation are increasingly emphasizing research using “big data.” The hope is that big data methods will both speed up research and make its results more applicable to “real-world” patients and health services. While big data research has been embraced by scientists, politicians, industry, and the public, numerous ethical, organizational, and technical/methodological concerns have also been raised. With respect to technical and methodological concerns, there is a view that these will be resolved through sophisticated information technologies, predictive algorithms, (...) and data analysis techniques. While such advances will likely go some way towards resolving technical and methodological issues, we believe that the epistemological issues raised by big data research have important ethical implications and raise questions about the very possibility of big data research achieving its goals. (shrink)

Medical necessity (claiming that a medical intervention or care is – at minimum – reasonable, appropriate and acceptable) depends on empirical evidence and on the interpretation of that evidence. Evidence and its interpretation define the standard of care. This commentary argues that both the evidence base and its interpretation are currently weak gatekeepers. Empirical meta-research suggests that very few medical interventions have high quality evidence in support of their effectiveness and very few of them also have relatively thorough assessments of (...) their potential harms. Therefore, evidence on the risk-benefit ratios carries almost always very large uncertainty. Arbitration about medical necessity is thus left to the interpretation process. Professional guidelines are notoriously unreliable and biased in this regard. Regulatory approval ends up being the key arbitrator, but over the years the regulatory process has been subverted. Regulatory approval currently does not mean that an intervention has a favourable risk-benefit ratio, but simply that it can be marketed, sold and made profit from. The process leads to a tragedy of commons where the final victim is society at large: medical necessity is invoked as an alibi for medicine to absorb societal resources. (shrink)

Neurobiology-based interventions for mental diseases and searches for useful biomarkers of treatment response have largely failed. Clinical trials should assess interventions related to environmental and social stressors, with long-term follow-up; social rather than biological endpoints; personalized outcomes; and suitable cluster, adaptive, and n-of-1 designs. Labor, education, financial, and other social/political decisions should be evaluated for their impacts on mental disease.

The linguistic foundations of science and technology include many terms that have been borrowed from ancient languages. In the case of terms with origins in the Greek language, the modern meaning can often differ significantly from the original one. Here we use the PubMed database to demonstrate the prevalence of words of Greek origin in the language of modern science, and call for scientists to exercise care when coining new terms.

Overstated generalizability is common in research. It may coexist with inflation of the magnitude and statistical support for effects and dismissal of internal validity problems. Generalizability may be secured before attempting replication of proposed discoveries or replication may precede efforts to generalize. These opposite approaches may decrease or increase, respectively, the use of inferential statistics with advantages and disadvantages.

In this issue of the Hastings Center Report, Jonathan Kimmelman and Alex London argue that in assessing the success of clinical translation, it is narrow‐minded to focus only on how many new drugs get licensed and how quickly they achieve licensure. Kimmelman and London show that clinical translation should be judged on its ability to generate as comprehensive an intervention ensemble as possible for the tested interventions. I would like to extend Kimmelman and London's position in two ways. First, I (...) would argue that in the current environment, failures should be seen not just as acceptable, but probably as the most useful outcomes that translational research efforts can offer. Second, an intervention ensemble probably cannot be generated with information only about the drug or drugs produced by a single company. For most conditions and diseases, there are already a large number of other interventions whose use is supported or contradicted by various levels of evidence. (shrink)

Antidepressants, in particular newer agents, are among the most widely prescribed medications worldwide with annual sales of billions of dollars. The introduction of these agents in the market has passed through seemingly strict regulatory control. Over a thousand randomized trials have been conducted with antidepressants. Statistically significant benefits have been repeatedly demonstrated and the medical literature is flooded with several hundreds of.