Aims Standardized immunosuppressive therapy (IS) had been previously investigated in
biopsy‐proven (BP) lymphocytic myocarditis with heart failure (HF). This study evaluated …

Background This study aimed to assess the impact of duration of early mobilisation on
survivors of critical illness. The hypothesis was that interventions lasting over 40 min, as per …

Studying causal effects of continuous treatments is important for gaining a deeper
understanding of many interventions, policies, or medications, yet researchers are often left …

Reweighting a distribution to minimize a distance to a target distribution is a powerful and
flexible strategy for estimating a wide range of causal effects, but can be challenging in …

With the modern software and online platforms to collect massive amount of data, there is an
increasing demand of applying causal inference methods at large scale when randomized …

Covariate balance is the degree to which the distribution of covariates is similar across
levels of the treatment. It has three main roles in causal effect estimation using matching: 1) …

Imbalance in covariate distributions leads to biased estimates of causal effects. Weighting
methods attempt to correct this imbalance but rely on specifying models for the treatment …

Maximum Mean Discrepancy (MMD) has been widely used in the areas of machine learning
and statistics to quantify the distance between two distributions in the p-dimensional …

Background New-onset atrial fibrillation (NOAF) is common in patients on an intensive care
unit (ICU). Evidence guiding treatments is limited, though recent reports suggest beta …

Objectives The current study aims to explore the relationship between the frequency of
volunteering and biological aging, as measured by epigenetic age acceleration. It also …