Sample size and power calculations for detecting changes in malaria transmission using antibody seroconversion rate

Area of Science:

• Epidemiology
• Biostatistics

Background:

• Serological data is crucial for monitoring malaria transmission intensity.
• Previous studies utilized serology as an adjunct measure without formal sample size calculations.

Purpose of the Study:

• To propose a sample size calculator for cross-sectional surveys to detect reductions in malaria transmission intensity.
• To evaluate the impact of known or unknown change points on sample size and estimation precision.

Main Methods:

• A reverse catalytic model and data simulation were used to develop the calculator.
• Logistic approximations for power curves were employed to detect changes in seroconversion rate (SCR).
• Sample sizes were illustrated for a hypothetical African population, considering known/unknown change points.

Main Results:

• Power is significantly influenced by the assumption of a known or unknown change point.
• Small sample sizes detect strong SCR reductions but yield imprecise estimates; precision control is key.
• Larger sample sizes are needed for subtle reductions, improving precision and reducing bias.

Conclusions:

• The proposed sample size calculator is applicable across diverse populations and survey types.
• Prior information on the change point can reduce sample size and estimation bias.
• The calculator offers a promising tool for malaria surveillance and control programs.