2 citations · 7 across the 8 of their papers we have counts for
8 papers
Flexible Method Comparison with the Probability of Agreement
Nathaniel T. Stevens
The comparison of methods of measurement is a common problem in clinical practice; as novel methods are developed, establishing their agreement with existing methods is crucial. Th…
Risk-Adjusted learning curve assessment using comparative probability metrics
Adel Ahmadi Nadi, Stefan Steiner, Nathaniel Stevens
Surgical learning curves are graphical tools used to evaluate a trainee's progress in the early stages of their career and determine whether they have achieved proficiency after co…
An Economical Approach to Design Posterior Analyses
Luke Hagar, Nathaniel T. Stevens
To design Bayesian studies, criteria for the operating characteristics of posterior analyses - such as power and the type I error rate - are often assessed by estimating sampling d…
From Augmentation to Decomposition: A New Look at CUPED in 2023
Alex Deng, Luke Hagar, Nathaniel Stevens +3
Ten years ago, CUPED (Controlled Experiments Utilizing Pre-Experiment Data) mainstreamed the idea of variance reduction leveraging pre-experiment covariates. Since its introduction…
Design of Bayesian A/B Tests Controlling False Discovery Rates and Power
Luke Hagar, Nathaniel T. Stevens
Businesses frequently run online controlled experiments (i.e., A/B tests) to learn about the effect of an intervention on multiple business metrics. To account for multiple hypothe…
Posterior Ramifications of Prior Dependence Structures
Luke Hagar, Nathaniel T. Stevens
Prior elicitation methods for Bayesian analyses transfigure prior information into quantifiable prior distributions. Recently, methods that leverage copulas have been proposed to a…
Bioequivalence Design with Sampling Distribution Segments
Luke Hagar, Nathaniel T. Stevens
In bioequivalence design, power analyses dictate how much data must be collected to detect the absence of clinically important effects. Power is computed as a tail probability in t…
Fast Power Curve Approximation for Posterior Analyses
Luke Hagar, Nathaniel T. Stevens
Bayesian hypothesis tests leverage posterior probabilities, Bayes factors, or credible intervals to inform data-driven decision making. We propose a framework for power curve appro…