The win odds is a distribution-free method of comparing locations of distributions of two independent random variables. Introduced as a method for analyzing hierarchical composite endpoints, it is well suited to be used in the analysis of ordinal scale endpoints in COVID-19 clinical trials. For a single outcome, we provide power and sample size calculation formulas for the win odds test. We also provide an implementation of the win odds analysis method for a single ordinal outcome in a commonly used statistical software to make the win odds analysis fully reproducible.

DARE-19 was a randomised, double-blind, placebo-controlled trial of patients hospitalised with COVID-19 and with at least one cardiometabolic risk factor (ie, hypertension, type 2 diabetes, atherosclerotic cardiovascular disease, heart failure, and chronic kidney disease). Patients were randomly assigned 1:1 to dapagliflozin (10 mg daily orally) or matched placebo for 30 days.

Dapagliflozin reduced the risk of total (first and repeat) HF hospitalizations and cardiovascular death. Time-to-first event analysis underestimated the benefit of dapagliflozin in HF and reduced ejection fraction.

DARE-19 will evaluate whether dapagliflozin can prevent COVID-19-related complications and all-cause mortality, or improve clinical recovery, and assess the safety profile of dapagliflozin in this patient population. Currently, DARE-19 is the first large randomized controlled trial investigating use of sodium-glucose cotransporter 2 inhibitors in patients with COVID-19.

The win ratio is a general method of comparing locations of distributions of two independent, ordinal random variables, and it can be estimated without distributional assumptions. In this paper we provide a unified theory of win ratio estimation in the presence of stratification and adjustment by a numeric variable.

This work is devoted to the questions of the statistics of stochastic processes. Particularly, the first chapter is devoted to a non-parametric estimation problem for an inhomogeneous Poisson process. The second chapter is dedicated to a problem of estimation of the solution of a Backward Stochastic Differential Equation (BSDE).