Seasonality, mediation and comparison (SMAC) methods to identify influences on lung function decline

Area of Science:

• Biostatistics
• Epidemiology
• Longitudinal Data Analysis

Background:

• Seasonal variations can significantly impact health markers, influencing disease progression and treatment effectiveness.
• Existing methods for analyzing longitudinal data often do not adequately account for complex seasonal patterns or facilitate cohort comparisons.
• Understanding these influences is crucial for accurate disease monitoring, particularly in chronic conditions like cystic fibrosis.

Purpose of the Study:

• To develop and present a comprehensive statistical methodology, termed seasonality, mediation and comparison (SMAC), for assessing seasonal influences on longitudinal markers.
• To extend current analytical approaches by integrating seasonality modeling with longitudinal correlation structures and mediation analysis.
• To provide a framework for comparing seasonal effects on health markers between different cohorts.

Main Methods:

• Development of a novel seasonality model combining sine-cosine functions with specialized covariance functions for longitudinal correlation.
• Integration of mediation analysis within the seasonality model to explore underlying mechanisms.
• Derivation of methods for comparing seasonality model estimates between cohorts using estimation coefficients.
• Application of the SMAC framework to lung function data from a cystic fibrosis cohort.

Main Results:

• The study successfully demonstrates a novel seasonality model for fitting lung function decline trajectories.
• It illustrates the steps for conducting mediation analyses within this seasonality framework.
• The derived calculations enable robust comparisons of seasonality models across different cohorts.

Conclusions:

• The seasonality, mediation and comparison (SMAC) method offers a powerful tool for analyzing seasonal influences on longitudinal health markers.
• SMAC provides a flexible approach applicable to various markers and disease contexts beyond the presented cystic fibrosis case study.
• This methodology enhances the ability to compare seasonal effects between cohorts, leading to more nuanced epidemiological insights.