Refuting "Debunking the GAMLSS Myth: Simplicity Reigns in Pulmonary Function Diagnostics"

TL;DR

This paper critically examines claims that segmented linear regression (SLR) matches GAMLSS in accuracy and is simpler, arguing that GAMLSS remains superior in predictive performance and accessibility despite its complexity.

Contribution

The authors challenge the notion that GAMLSS is overly complex and less accurate, defending its use in pulmonary function diagnostics through a detailed critique.

Findings

GAMLSS provides better prediction accuracy than SLR.

SLR is not as straightforward or accessible as claimed.

GAMLSS offers more comprehensive modeling capabilities.

Abstract

We read with interest the above article by Zavorsky (2025, Respiratory Medicine, doi:10.1016/j.rmed.2024.107836) concerning reference equations for pulmonary function testing. The author compares a Generalized Additive Model for Location, Scale, and Shape (GAMLSS), which is the standard adopted by the Global Lung Function Initiative (GLI), with a segmented linear regression (SLR) model, for pulmonary function variables. The author presents an interesting comparison; however there are some fundamental issues with the approach. We welcome this opportunity for discussion of the issues that it raises. The author's contention is that (1) SLR provides "prediction accuracies on par with GAMLSS"; and (2) the GAMLSS model equations are "complicated and require supplementary spline tables", whereas the SLR is "more straightforward, parsimonious, and accessible to a broader audience". We respectfully disagree with both of these points.