Sepúlveda Nuno, Paulino Carlos Daniel, Drakeley Chris
London School of Hygiene and Tropical Medicine, Keppel Street, London, WC1E 7HT, UK.
Center of Statistics and Applications of University of Lisbon, Faculdade de Ciências da Universidade de Lisboa, Bloco C6-Piso 4, 1749-1016, Lisbon, Portugal.
Malar J. 2015 Dec 30;14:529. doi: 10.1186/s12936-015-1050-3.
Several studies have highlighted the use of serological data in detecting a reduction in malaria transmission intensity. These studies have typically used serology as an adjunct measure and no formal examination of sample size calculations for this approach has been conducted.
A sample size calculator is proposed for cross-sectional surveys using data simulation from a reverse catalytic model assuming a reduction in seroconversion rate (SCR) at a given change point before sampling. This calculator is based on logistic approximations for the underlying power curves to detect a reduction in SCR in relation to the hypothesis of a stable SCR for the same data. Sample sizes are illustrated for a hypothetical cross-sectional survey from an African population assuming a known or unknown change point.
Overall, data simulation demonstrates that power is strongly affected by assuming a known or unknown change point. Small sample sizes are sufficient to detect strong reductions in SCR, but invariantly lead to poor precision of estimates for current SCR. In this situation, sample size is better determined by controlling the precision of SCR estimates. Conversely larger sample sizes are required for detecting more subtle reductions in malaria transmission but those invariantly increase precision whilst reducing putative estimation bias.
The proposed sample size calculator, although based on data simulation, shows promise of being easily applicable to a range of populations and survey types. Since the change point is a major source of uncertainty, obtaining or assuming prior information about this parameter might reduce both the sample size and the chance of generating biased SCR estimates.
多项研究强调了血清学数据在检测疟疾传播强度降低方面的应用。这些研究通常将血清学作为一种辅助措施，且尚未对该方法的样本量计算进行正式检验。
针对横断面调查，提出了一种样本量计算器，该计算器使用反向催化模型的数据模拟，假设在采样前给定的变化点血清转化率（SCR）降低。此计算器基于潜在功效曲线的逻辑近似值，以检测相对于相同数据稳定SCR假设下SCR的降低情况。针对来自非洲人群的假设横断面调查，给出了已知或未知变化点情况下的样本量示例。
总体而言，数据模拟表明，假设已知或未知变化点对功效有很大影响。小样本量足以检测SCR的大幅降低，但必然会导致当前SCR估计的精度较差。在这种情况下，通过控制SCR估计的精度来更好地确定样本量。相反，检测疟疾传播更细微的降低需要更大的样本量，但这些样本量必然会提高精度，同时减少假定的估计偏差。
所提出的样本量计算器虽然基于数据模拟，但显示出易于应用于一系列人群和调查类型的前景。由于变化点是不确定性的主要来源，获取或假设有关此参数的先验信息可能会减少样本量以及产生有偏差的SCR估计的可能性。
