Transforming the interpretation of significance in heart failure trials

Impact of redefining statistical significance on P-hacking and false positive rates: An agent-based model

In recent years, concern has grown about the inappropriate application and interpretation of P values, especially the use of P<0.05 to denote "statistical significance" and the practice of P-hacking to produce results below this threshold and selectively reporting these in publications. Such behavior is said to be a major contributor to the large number of false and non-reproducible discoveries found in academic journals. In response, it has been proposed that the threshold for statistical significance be changed from 0.05 to 0.005. The aim of the current study was to use an evolutionary agent-based model comprised of researchers who test hypotheses and strive to increase their publication rates in order to explore the impact of a 0.005 P value threshold on P-hacking and published false positive rates. Three scenarios were examined, one in which researchers tested a single hypothesis, one in which they tested multiple hypotheses using a P<0.05 threshold, and one in which they tested multiple hypotheses using a P<0.005 threshold. Effects sizes were varied across models and output assessed in terms of researcher effort, number of hypotheses tested and number of publications, and the published false positive rate. The results supported the view that a more stringent P value threshold can serve to reduce the rate of published false positive results. Researchers still engaged in P-hacking with the new threshold, but the effort they expended increased substantially and their overall productivity was reduced, resulting in a decline in the published false positive rate. Compared to other proposed interventions to improve the academic publishing system, changing the P value threshold has the advantage of being relatively easy to implement and could be monitored and enforced with minimal effort by journal editors and peer reviewers.

Spin in the Scientific Literature on Bariatric Endoscopy: a Systematic Review of Randomized Controlled Trials

Bariatric endoscopy (BE) is an emerging treatment option for people with obesity. Spin (i.e., the practice of frequent misrepresentation or overinterpretation of study findings) may lead to imbalanced and unjustified optimism in the interpretation of the results. The aim of this systematic review was to determine the frequency and type of spin in randomized controlled trials (RCTs) of endoscopic primary weight loss techniques with statistically significant and nonsignificant primary outcomes. In conclusion, spin is observed in the abstract and main text of BE reports and can lead to misinterpretation or overinterpretation of the results. Since BE challenges the available non-endoscopic treatments for obesity, further research is needed to better qualify these techniques, as being effective and safe, as well as predefined hypotheses and analyses.

Machine learning, artificial intelligence and mechanical circulatory support: A primer for clinicians

Artificial intelligence (AI) refers to the ability of machines to perform intelligent tasks, and machine learning (ML) is a subset of AI describing the ability of machines to learn independently and make accurate predictions. The application of AI combined with "big data" from the electronic health records, is poised to impact how we take care of patients. In recent years, an expanding body of literature has been published using ML in cardiovascular health care, including mechanical circulatory support (MCS). This primer article provides an overview for clinicians on relevant concepts of ML and AI, reviews predictive modeling concepts in ML and provides contextual reference to how AI is being adapted in the field of MCS. Lastly, it explains how these methods could be incorporated in the practices of medicine to improve patient outcomes.