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Score Test for Functional Markov Process With Image Predictor.

Yang Wang¹, Graham A Colditz¹, Shu Jiang¹

¹Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA.

Statistics in Medicine

|August 16, 2025

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This study introduces a new model to analyze disease progression using high-dimensional image data. Mammogram images are associated with breast cancer transition, aiding in early detection and risk assessment.

Area of Science:

Biostatistics
Medical Imaging Analysis
Cancer Research

Background:

Disease progression modeling is crucial for understanding and predicting health outcomes.
High-dimensional data, such as medical images, present unique challenges in statistical analysis.
Intermittent observation and interval-censored data are common in longitudinal health studies.

Purpose of the Study:

To develop a statistical model for disease transition analysis incorporating high-dimensional image predictors.
To quantify the association between mammogram image features and breast cancer progression.
To test the significance of image data in predicting transitions between disease states.

Main Methods:

A functional multistate model based on Markov processes was developed.

Keywords:

breast cancer functional data image data multistate model score test

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The model accommodates interval-censored data and intermittent observations.

A score test was derived to assess the association of image predictors with disease state transitions.

Main Results:

The study successfully quantified the association between mammogram images and breast cancer state transitions.
The developed score test provides asymptotic distribution for statistical inference.
Results indicate a significant link between mammogram image characteristics and breast cancer development probability.

Conclusions:

The proposed model effectively integrates high-dimensional image data into disease progression analysis.
Mammogram image features are important predictors for breast cancer transition.
This approach offers a novel method for risk assessment and early detection in breast cancer.