Confounding Adjustment

What problem does it solve?

This Skill addresses the critical challenge of controlling for time-varying confounders in environmental time series studies, ensuring more accurate estimations of exposure-health relationships.

Core Features & Use Cases

• Trend and Seasonality Adjustment: Implements flexible splines (ns()) to model and remove long-term trends and seasonal patterns.
• Meteorological Confounder Control: Provides methods for adjusting for temperature and humidity using natural splines or cross-basis functions.
• Calendar Effect Integration: Includes options for incorporating day-of-week, public holidays, and other calendar-related factors.
• Use Case: When analyzing the impact of air pollution on respiratory hospital admissions, this Skill helps adjust for daily temperature fluctuations and yearly seasonal trends that could otherwise bias the pollution effect estimate.

Quick Start

Use the confounding adjustment skill to control for a long-term trend and seasonality using 7 degrees of freedom per year in your R model.