Deeply-Learned Generalized Linear Models with Missing Data

TL;DR

This paper introduces dlglm, a novel deep learning architecture designed to handle both ignorable and non-ignorable missing data in supervised learning, demonstrating superior performance over existing methods in simulations and a real-world case study.

Contribution

The paper presents dlglm, a new deep learning model that effectively manages complex missing data patterns in biomedical datasets, advancing the handling of MNAR data in supervised learning.

Findings

dlglm outperforms existing methods in simulations with MNAR missingness.

dlglm successfully applied to a real-world bank marketing dataset.

The architecture flexibly models both ignorable and non-ignorable missing data patterns.

Abstract

Deep Learning (DL) methods have dramatically increased in popularity in recent years, with significant growth in their application to supervised learning problems in the biomedical sciences. However, the greater prevalence and complexity of missing data in modern biomedical datasets present significant challenges for DL methods. Here, we provide a formal treatment of missing data in the context of deeply learned generalized linear models, a supervised DL architecture for regression and classification problems. We propose a new architecture, \textit{dlglm}, that is one of the first to be able to flexibly account for both ignorable and non-ignorable patterns of missingness in input features and response at training time. We demonstrate through statistical simulation that our method outperforms existing approaches for supervised learning tasks in the presence of missing not at random (MNAR) missingness. We conclude with a case study of a Bank Marketing dataset from the UCI Machine Learning Repository, in which we predict whether clients subscribed to a product based on phone survey data. Supplementary materials for this article are available online.