Hyperbolic statistical inference for Treatment Effects with Circular biomarker of astigmatism

TL;DR

This paper introduces a novel hyperbolic geometry-based statistical testing framework for circular biomedical data, specifically angular measurements like astigmatism, providing more accurate and interpretable analysis compared to existing methods.

Contribution

The paper develops a new hyperbolic geometry approach for two-sample testing of circular data, embedding von Mises distributions into the Poincaré disk to improve comparison and inference.

Findings

Demonstrates stable empirical size and strong consistency in simulations

Shows superior asymptotic power over existing methods

Successfully applied to ophthalmology dataset for astigmatism analysis

Abstract

Circular biomarkers arise naturally in many biomedical applications, particularly in ophthalmology, where angular measurements such as astigmatism are routinely recorded. Similar directional variables also occur in the study of human body rotations, including movements of the hand, waist, neck, and lower limbs. Motivated by a clinical dataset comprising angular measurements of astigmatism induced by two cataract surgery procedures, we propose a novel two-sample testing framework for circular data grounded in hyperbolic geometry. Assuming von Mises distributions with either common or group-specific concentration parameters, we embed the corresponding parameter spaces into the Poincar\'e disk, an open unit disk endowed with the Poincar\'e metric.Under this construction, each von Mises distribution is mapped uniquely to a point in the Poincar\'e disk, yielding a continuous geometric representation that preserves the intrinsic structure of the parameter space. This embedding enables direct comparison of group distributions via hyperbolic distances, leading to natural and interpretable test statistics. We develop permutation-based tests for the common concentration case and bootstrap-based procedures for unequal concentrations. Extensive simulation studies demonstrate stable empirical size, strong consistency, and superior asymptotic power compared with existing competing methods. The proposed methodology is illustrated through a detailed analysis of the cataract surgery dataset, including a clinically informed restructuring of the original observations. The results highlight the practical advantages of incorporating hyperbolic geometry into the analysis of circular biomedical data and underscore the potential of geometry-aware inference for directional biomarkers.