Estimating disease prevalence using relatives of case and control probands

Area of Science:

• Epidemiology
• Biostatistics
• Genetic Epidemiology

Background:

• Case-control family studies are crucial for understanding disease familial aggregation.
• Existing methods may have limitations in accurately estimating disease prevalence, especially for rare conditions.
• Accurate prevalence estimation is vital for public health planning and resource allocation.

Purpose of the Study:

• To introduce novel estimators for overall and covariate-stratum-specific disease prevalence.
• To develop confidence intervals with good coverage properties, particularly for low prevalence diseases.
• To evaluate the performance of these estimators and intervals using simulation studies.

Main Methods:

• Developed estimators that integrate data from affected and unaffected relatives of both case and control probands.
• Designed confidence intervals to ensure reliable estimation even with small prevalences.
• Conducted simulation experiments on case-control family data from populations with varying familial aggregation levels.

Main Results:

• The proposed estimators yielded approximately unbiased estimates of population prevalence across different familial aggregation levels.
• Estimates showed close and symmetric variation around true population counterparts.
• Confidence intervals demonstrated good coverage properties, even with small sample sizes and low prevalences.

Conclusions:

• The new method provides a robust approach for estimating disease prevalence from case-control family study data.
• The estimators and confidence intervals are effective even for rare diseases and varying degrees of familial aggregation.
• Consideration of underlying assumptions is important, with potential for alternative estimators in specific scenarios.