Regression methods for assessing familial aggregation of disease

Area of Science:

• Epidemiology
• Biostatistics
• Genetic Epidemiology

Background:

• Familial aggregation studies are crucial for understanding disease etiology.
• Logistic regression models offer a statistical framework for analyzing disease risk in families.
• Case-control designs are commonly used to investigate disease associations.

Purpose of the Study:

• To review and present methods for assessing familial aggregation of disease using logistic regression.
• To extend these methods for analyzing the co-aggregation of two disorders.
• To provide practical examples using real-world case-control data.

Main Methods:

• Utilizing simple logistic regression models for familial aggregation analysis.
• Employing a case-control sampling design with data on first-degree relatives.
• Developing and applying 'proband predictive' and 'family predictive' models.
• Extending methods to characterize the co-aggregation of two disorders.

Main Results:

• Demonstrated the application of logistic regression for familial aggregation in a lung cancer case-control study.
• Illustrated the analysis of co-aggregation for eating disorders and depression.
• Provided a framework for assessing shared genetic or environmental factors.

Conclusions:

• Logistic regression models provide a flexible approach to studying familial disease aggregation.
• The presented methods are valuable for investigating complex disease patterns and co-morbidities.
• The study highlights the utility of case-control designs in genetic epidemiology.