Efficient study designs to assess the accuracy of screening tests

Area of Science:

• Biostatistics
• Medical Diagnostics
• Health Economics

Background:

• Accurate estimation of screening test performance (sensitivity, specificity) is crucial.
• Reference standard verification is often the primary cost driver in test evaluation.
• Minimizing sample size for verification is key to cost-effective evaluations.

Purpose of the Study:

• To determine the optimal sampling strategy for test positives and negatives to minimize total sample size for verification.
• To achieve narrow confidence intervals for test sensitivity at minimal cost.
• To provide practical guidance for cost-efficient screening test evaluations.

Main Methods:

• Utilized formulae from Begg and Greenes (1983) for optimal sampling.
• Calculated optimal sampling fractions based on desired confidence interval width for sensitivity.
• Analyzed the impact of varying sensitivity and specificity on sampling strategies and cost savings.

Main Results:

• Optimal sampling often requires verifying more test positives and fewer test negatives than equal sampling fractions, unless sensitivity is very high.
• For sensitivity=0.7 and specificity=0.99, optimal sampling involves 6.2% test positives vs. 1.7% with equal fractions.
• Significant cost savings (up to 50%) are achievable for lower sensitivities (e.g., 0.3), with negligible savings for high sensitivities (>0.8).

Conclusions:

• Adjusting sampling fractions for test positives and negatives is an effective strategy for reducing costs in screening test evaluations.
• Optimal sampling strategies primarily impact sensitivity estimation and cost-efficiency, with minimal effect on specificity confidence intervals.
• The study provides figures to guide the selection of optimal sampling strategies based on estimated specificity and sensitivity ranges.