Sensitivity Analysis

Conduct sensitivity analyses for DLNM studies by varying parameters and confounders.

Updated Mar 7, 2026

Search Tags:#robustness #sensitivity analysis #epidemiology #model validation #dlnm #subgroup analysis

Ready to run

npx skills add https://github.com/ntluong95/agent-skills-statistics --skill sensitivity-analysis-ntluong95

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: Sensitivity Analysis
Source: https://github.com/ntluong95/agent-skills-statistics/tree/main/skills/epidemiology/sensitivity-analysis
Command: npx skills add https://github.com/ntluong95/agent-skills-statistics --skill sensitivity-analysis-ntluong95

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the need to ensure the reliability and robustness of Distributed Lag Non-Linear Models (DLNM) by systematically testing how results change under various analytical choices.

Core Features & Use Cases

Parameter Variation: Explore the impact of changing degrees of freedom for exposure-response and lag-response, and varying maximum lag periods.
Confounding Adjustment: Test sensitivity by adding/removing confounders like humidity, holidays, or stratifying by season.
Subgroup Analysis: Evaluate model stability across different demographic groups (age, sex) or causes of death.
Multi-City Robustness: Perform leave-one-out analyses in meta-analyses to identify influential cities.
Use Case: A researcher has built a DLNM model for air pollution and health. They can use this skill to demonstrate that their main findings remain consistent even when they slightly alter the model's parameters or adjust for different sets of confounders.

Quick Start

Use the sensitivity analysis skill to check how the model's results change when the degrees of freedom for the exposure-response curve are varied between 2 and 6.

Frequently Asked Questions about Sensitivity Analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I test DLNM model robustness when varying degrees of freedom?▼

Test DLNM robustness by systematically varying degrees of freedom for the exposure-response and lag-response curves, typically between 2 and 6, to confirm findings remain consistent under different analytical choices.

What is sensitivity analysis in epidemiology model validation?▼

Sensitivity analysis in epidemiology model validation evaluates the reliability of findings by testing result stability across varying maximum lags, confounding adjustments, and demographic subgroup stratifications.

How do I perform a multi-city leave-one-out sensitivity analysis?▼

Perform a multi-city leave-one-out sensitivity analysis by iteratively excluding individual cities to identify influential outliers and assess the overall robustness of the pooled model estimates.

How do I adjust for confounding in DLNM sensitivity checks?▼

Adjust for confounding in DLNM sensitivity checks by adding or removing variables like humidity and holidays, or by stratifying by season to test the stability of the main exposure-response relationship.

Can I evaluate model stability across different age subgroups in DLNM?▼

Yes, you can evaluate DLNM model stability by performing subgroup analyses across different demographic groups, such as age and sex, or by different causes of death to ensure robustness in specific populations.