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

Towards Ocean Grazer's Modular Power Take-Off System Modeling:A Port-Hamiltonian Approach

This paper presents a modular modeling framework for the Ocean Grazer's Power Take-Off (PTO) system, which operates as an array of point-absorber type devices connected to a hydraulic system. The modeling is based on the port-Hamiltonian (PH) framework that enables energy-based analysis and control of the PTO system. Firstly, a modular model of a point-absorber hydraulic system, which represents the main building block of the PTO, is presented. The model consists of wave-mechanical and hydraulic subsystems that are interconnected with a transformer-type interconnection. Secondly, we show passivity of the point-absorber hydraulic element and the accumulation of potential energy, which is due to the novel pumping mechanism of the point-absorber. Finally, we illustrate these properties through simulation results

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