Contrasting Global and Patient-Specific Regression Models via a Neural Network Representation

TL;DR

This paper introduces a neural network-based diagnostic tool that contrasts global and patient-specific regression models, identifying subgroups where personalized models outperform global ones, especially in clinical settings like COPD.

Contribution

The paper proposes a novel neural network approach using autoencoders for dimension reduction to effectively contrast global and local regression models in high-dimensional predictor spaces.

Findings

Global models are adequate for most patients but fail for specific subgroups.

The proposed method successfully identifies and characterizes patient subgroups with different outcome associations.

Mapping subgroup models back to original predictors provides interpretability and insights.

Abstract

When developing clinical prediction models, it can be challenging to balance between global models that are valid for all patients and personalized models tailored to individuals or potentially unknown subgroups. To aid such decisions, we propose a diagnostic tool for contrasting global regression models and patient-specific (local) regression models. The core utility of this tool is to identify where and for whom a global model may be inadequate. We focus on regression models and specifically suggest a localized regression approach that identifies regions in the predictor space where patients are not well represented by the global model. As localization becomes challenging when dealing with many predictors, we propose modeling in a dimension-reduced latent representation obtained from an autoencoder. Using such a neural network architecture for dimension reduction enables learning a latent representation simultaneously optimized for both good data reconstruction and for revealing local outcome-related associations suitable for robust localized regression. We illustrate the proposed approach with a clinical study involving patients with chronic obstructive pulmonary disease. Our findings indicate that the global model is adequate for most patients but that indeed specific subgroups benefit from personalized models. We also demonstrate how to map these subgroup models back to the original predictors, providing insight into why the global model falls short for these groups. Thus, the principal application and diagnostic yield of our tool is the identification and characterization of patients or subgroups whose outcome associations deviate from the global model.