In a new draft guidance issued on January 14, 2026, the FDA discussed the use of a modern statistical methodology in clinical trials designed to ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision-making. In both traditional ...

A relatively simple statistical analysis method can more accurately predict the risk of landslides caused by heavy rain, according to a study coordinated by Brazilian researchers affiliated with the ...

A deep learning framework combines convolutional and bidirectional recurrent networks to improve protein function prediction from genomic ...

To claim federal income tax credits for a research project, a taxpayer must prove that the project satisfies each prong of a four-part test. In IRS Field Attorney Advisory 20212501F (June 25, 2021), ...

Empirical likelihood methods have emerged as a robust, non‐parametric framework for statistical inference that skilfully bypasses the need for strong parametric assumptions. By constructing likelihood ...

Researchers from Peking University have conducted a comprehensive systematic review on the integration of machine learning into statistical methods for disease risk prediction models, shedding light ...

Our laboratory has developed a range of data analysis workflows that incorporate advanced statistical and computational methods to interpret the complex molecular datasets generated by MS technologies ...

The Center for Clinical and Translational Science (CCTS) is pleased to announce the latest recipients of CCTS Statistical and Analytic Methods Development support. This competitive program is designed ...