2 citations · 5 across the 14 of their papers we have counts for
15 papers
Bayesian Sample Size Determination: Sampling Distribution Estimation or Exploration?
Luke Hagar, Paul Gustafson
To design many Bayesian studies, the sample size is chosen to attain sufficient power to reject a null hypothesis or a desired probability that an interval estimate is sufficiently…
COBRA-DOSE: Copula-based Bayesian Model Averaging for Dose Selection
Luke Hagar, Min Zhang, Ranjeny Thomas +1
Early-phase clinical trials for dose selection typically enrol few patients and aim to identify doses that are both safe and promising for further study. While traditional approach…
Computationally Efficient Experimental Design with Generalized Posteriors
Luke Hagar, James M. McGree
The hybrid approach to experimental design aims to control frequentist operating characteristics of Bayesian decision procedures. These operating characteristics are assessed by si…
An Efficient Framework for Robust Sample Size Determination
Luke Hagar, Andrew J. Martin
In many settings, robust data analysis involves computational methods for uncertainty quantification and statistical inference. To design frequentist studies that leverage robust a…
Bayesian Design of Experiments in the Presence of Nuisance Parameters
Shirin Golchi, Luke Hagar
Design of experiments has traditionally relied on the frequentist hypothesis testing framework where the optimal size of the experiment is specified as the minimum sample size that…
An Efficient Approach to Design Bayesian Platform Trials
Luke Hagar, Lara Maleyeff, Shirin Golchi +1
Platform trials evaluate multiple experimental treatments against a common control group (and/or against each other), which often reduces the trial duration and sample size. Bayesi…
Classifying group actions on hyperbolic spaces
D. Osin, K. Oyakawa, A. Rapinchuk
For a given group , it is natural to ask whether one can classify all isometric -actions on Gromov hyperbolic spaces. We propose a formalization of this problem utilizing the…
Design of Bayesian Clinical Trials with Clustered Data
Luke Hagar, Shirin Golchi
In the design of clinical trials, it is essential to assess the design operating characteristics (e.g., power and the type I error rate). Common practice for the evaluation of oper…
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…
An Economical Approach to Design with Precision Criteria
Luke Hagar, Nathaniel T. Stevens
Estimation frameworks for statistical inference are preferred to hypothesis testing when quantifying uncertainty and precise estimation are more valuable than binary decisions abou…