Improving the analysis of composite endpoints in rare disease trials

Background: Composite endpoints are recommended in rare diseases to increase power and/or to sufficiently capture complexity. Often, they are in the form of responder indices which contain a mixture of continuous and binary components. Analyses of these outcomes typically treat them as binary, thus only using the dichotomisations of continuous components. The augmented binary method offers a more efficient alternative and is therefore especially useful for rare diseases. Previous work has indicated the method may have poorer statistical properties when the sample size is small. Here we investigate small sample properties and implement small sample corrections. Methods: We re-sample from a previous trial with sample sizes varying from 30 to 80. We apply the standard binary and augmented binary methods and determine the power, type I error rate, coverage and average confidence interval width for each of the estimators. We implement Firth's adjustment for the binary component models and a small sample variance correction for the generalized estimating equations, applying the small sample adjusted methods to each sub-sample as before for comparison. Results: For the log-odds treatment effect the power of the augmented binary method is 20-55% compared to 12-20% for the standard binary method. Both methods have approximately nominal type I error rates. The difference in response probabilities exhibit similar power but both unadjusted methods demonstrate type I error rates of 6-8%. The small sample corrected methods have approximately nominal type I error rates. On both scales, the reduction in average confidence interval width when using the adjusted augmented binary method is 17-18%. This is equivalent to requiring a 32% smaller sample size to achieve the same statistical power. Conclusions: The augmented binary method with small sample corrections provides a substantial improvement for rare disease trials using composite endpoints. We recommend the use of the method for the primary analysis in relevant rare disease trials. We emphasise that the method should be used alongside other efforts in improving the quality of evidence generated from rare disease trials rather than replace them

Agent based demand flexibility management for wind power forecasting error mitigation using the SG-BEMS framework

The integration process of renewable energy sources (RES) and distributed energy resources (DER) into the power system, is characterized by concerns that originate from their stochastic and uncontrollable nature. This means that system operators require reliable forecasting tools, in order to ensure efficient and reliable operation. Accordingly, this paper proposes the use of demand flexibility, to counteract the RES forecasting errors. For this purpose, distributed and decentralized intelligence is used, via the SG-BEMS framework, to invoke demand flexibility in a timely and effective fashion, while taking into account the negative effects on the building occupants comfort. Lastly, numerical results from a simulated case of study are presented, which confirm that demand flexibility can be used to mitigate the magnitude of forecast errors

Artificial Intelligence For The Discovery Of Novel Antimicrobial Agents For Emerging Infectious Diseases

The search for effective drugs to treat new and existing diseases is a laborious one requiring a large investment of capital, resources, and time. The coronavirus 2019 (COVID-19) pandemic has been a painful reminder of the lack of development of new antimicrobial agents to treat emerging infectious diseases. Artificial intelligence (AI) and other in silico techniques can drive a more efficient, cost friendly approach to drug discovery by helping move potential candidates with better clinical tolerance forward in the pipeline. Several research teams have developed successful AI platforms for hit identification, lead generation, and lead optimization. In this review, we investigate the technologies at the forefront of spearheading an AI revolution in drug discovery and pharmaceutical sciences

The Driving Behavior Survey: scale construction and validation.

To access publisher full text version of this article. Please click on the hyperlink in Additional Links field.Although long recognized in the clinical literature, problematic behavior characteristic of anxious drivers has received little empirical attention. The current research details development of a measure of anxious driving behavior conducted across three studies. Factor analytic techniques identified three dimensions of maladaptive behaviors across three college samples: anxiety-based performance deficits, exaggerated safety/caution behavior, and anxiety-related hostile/aggressive behavior. Performance deficits evidenced convergent associations with perceived driving skill and were broadly related to driving fear. Safety/caution behaviors demonstrated convergence with overt travel avoidance, although this relationship was inconsistent across studies. Safety/caution scores were associated specifically with accident- and social-related driving fears. Hostile/aggressive behaviors evidenced convergent relationships with driving anger and were associated specifically with accident-related fear. Internal consistencies were adequate, although some test-retest reliabilities were marginal in the unselected college sample. These data provide preliminary evidence for utility of the measure for both research and clinical practice