Immune checkpoint inhibitors (ICIs) and vascular endothelial growth factor receptor tyrosine kinase inhibitors are cornerstones of first-line treatment for advanced renal cell carcinoma; however, optimal treatment sequencing after progression is unknown. This study aimed to assess clinical outcomes of tivozanib-nivolumab versus tivozanib monotherapy in patients with metastatic renal cell carcinoma who have progressed following one or two lines of therapy in the post-ICI setting.

One in five people are at high risk for atherosclerotic cardiovascular disease and aortic valve stenosis due to high lipoprotein(a). Lipoprotein(a) concentrations are lowest in people from east Asia, Europe, and southeast Asia, intermediate in people from south Asia, the Middle East, and Latin America, and highest in people from Africa. Concentrations are more than 90% genetically determined and 17% higher in post-menopausal women than in men. Individuals at a higher cardiovascular risk should have lipoprotein(a) concentrations measured once in their lifetime to inform those with high concentrations to adhere to a healthy lifestyle and receive medication to lower other cardiovascular risk factors. With no approved drugs to lower lipoprotein(a) concentrations, it is promising that at least five drugs in development lower concentrations by 65-98%, with three currently being tested in large cardiovascular endpoint trials. This Review covers historical perspectives, physiology and pathophysiology, genetic evidence of causality, epidemiology, role in familial hypercholesterolaemia and diabetes, management, screening, diagnosis, measurement, prevention, and future lipoprotein(a)-lowering drugs.

Frequentist and Bayesian statistics represent two differing paradigms for the analysis of data. Frequentism became the dominant mode of statistical thinking in medical practice during the 20th century. The advent of modern computing has made Bayesian analysis increasingly accessible, enabling growing use of Bayesian methods in a range of disciplines, including medical research. Rather than conceiving of probability as the expected frequency of an event (purported to be measurable and objective), Bayesian thinking conceives of probability as a measure of strength of belief (an explicitly subjective concept). Bayesian analysis combines previous information (represented by a mathematical probability distribution, the prior) with information from the study (the likelihood function) to generate an updated probability distribution (the posterior) representing the information available for clinical decision making. Owing to its fundamentally different conception of probability, Bayesian statistics offers an intuitive, flexible, and informative approach that facilitates the design, analysis, and interpretation of clinical trials. In this Review, we provide a brief account of the philosophical and methodological differences between Bayesian and frequentist approaches and survey the use of Bayesian methods for the design and analysis of clinical research.