Empirical Bayes is a versatile approach to “learn from a lot” in two ways: first, from a large number of variables and, second, from a potentially large amount of prior information, for example, ...

FDA proposes framework clinical trial designs to guide Bayesian methods, improving efficiency in drug development for rare and pediatric conditions.

The FDA’s new draft guidance on Bayesian methodology signals a shift toward more flexible, data-driven clinical trial designs, enabling sponsors to use prior data and adaptive approaches to improve ...

Bayesian methods have emerged as a pivotal framework in the design and analysis of clinical trials, offering a systematic approach for updating evidence as new data become available. By utilising ...

Symmetries in nature make things beautiful; symmetries in data make data handling efficient. However, the complexity of identifying such patterns in data has always bedeviled researchers. Scientists ...

Articulate the primary interpretations of probability theory and the role these interpretations play in Bayesian inference Use Bayesian inference to solve real-world statistics and data science ...

Bayesian methods in Structural Equation Modeling (SEM) represent a paradigm shift in statistical analysis, integrating prior beliefs with empirical data to derive robust parameter estimates. This ...

Improved Cancer-Specific Risk Stratification by the Lymph Node Ratio-Based Nomogram: A Potential Role in Guiding Postoperative Management Decisions for Oral Cavity Carcinoma We propose a novel ...

Patients were stratified to cohort A (unspecified tumors) or cohort B (rare genomic alterations). The TARGET-CRM design permits cohort B patients to immediately enroll at one dose level below the ...

In recent years, something unexpected has been happening in artificial intelligence. Modern AI appears to be breaking a rule that statisticians have preached for nearly a century: Keep models in a ...