Highly Irregular Functional Generalized Linear Regression with Electronic Health Records

TL;DR

This paper introduces MISFIT, a novel method for fitting generalized functional linear regression models to irregularly sampled electronic health record data, enabling consistent estimation and uncertainty quantification without requiring dense sampling.

Contribution

MISFIT is a new multiple imputation-based approach that allows for consistent estimation in sparse, irregular data and propagates uncertainty, improving analysis of electronic health records.

Findings

Demonstrated association between macrocephaly development and head growth metrics

Enabled accurate modeling with highly variable sampling patterns

Provided uncertainty estimates for regression parameters

Abstract

This work presents a new approach, called MISFIT, for fitting generalized functional linear regression models with sparsely and irregularly sampled data. Current methods do not allow for consistent estimation unless one assumes that the number of observed points per curve grows sufficiently quickly with the sample size. In contrast, MISFIT is based on a multiple imputation framework, which has the potential to produce consistent estimates without such an assumption. Just as importantly, it propagates the uncertainty of not having completely observed curves, allowing for a more accurate assessment of the uncertainty of parameter estimates, something that most methods currently cannot accomplish. This work is motivated by a longitudinal study on macrocephaly, or atypically large head size, in which electronic medical records allow for the collection of a great deal of data. However, the sampling is highly variable from child to child. Using MISFIT we are able to clearly demonstrate that the development of pathologic conditions related to macrocephaly is associated with both the overall head circumference of the children as well as the velocity of their head growth.