Covariate Selection from Data Collection Onwards: A Methodology for Neurosurgeons.

Area of Science:

• Epidemiology
• Biostatistics
• Clinical Research

Background:

• Covariate selection is crucial for epidemiologic and clinical studies.
• Existing literature often focuses on data analysis, neglecting data collection principles.
• Jargon in current guidelines limits accessibility for many researchers.

Purpose of the Study:

• To provide a foundational guide for primary data collection on covariates.
• To clarify covariate selection principles for both data collection and analysis.
• To offer a clear, jargon-free framework for researchers.

Main Methods:

• Categorized covariates into four essential groups for measurement: common causes, exposure causes, outcome causes, and sociodemographic/baseline.
• Developed a conceptual framework for covariate inclusion/exclusion in regression models.
• Emphasized clear communication, avoiding advanced causal inference terminology.

Main Results:

• Proposed four key covariate sets for data collection: common causes (exposure and outcome), exposure causes, outcome causes, and sociodemographic/baseline.
• Recommended regression modeling practices: include common causes and sociodemographics, exclude colliders, and maintain a 10:1 observation-to-term ratio.

Conclusions:

• Effective covariate selection during data collection enhances study validity.
• A clear framework for covariate selection and modeling is essential for robust epidemiologic and clinical research.
• Accessible guidelines are needed to improve the quality of research data and analysis.