Sample size methods for estimating HIV incidence from cross-sectional surveys.
Sample size methods for estimating HIV incidence from cross-sectional surveys.
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DOI：
10.1111/biom.12336
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发表时间：
2015-12
期刊：
影响因子：
1.9
通讯作者：
Brookmeyer R
中科院分区：
文献类型：
--
作者：
Konikoff J;Brookmeyer R
了解艾滋病毒发病率，即人口中新感染的发生率，对于跟踪和监测这一流行病至关重要。在本文中，我们推导了确定横断面调查样本大小的方法，以足够精确地估计发病率。我们进一步展示了如何为两个连续的横断面调查指定样本量，以在足够的功率下检测发生率的变化。在这些调查中，使用诸如CD4细胞计数、病毒载量和最近开发的血清学测定等生物标志物来确定哪些个体处于感染的早期疾病阶段。这一阶段的总人数除以未受感染的人数，可以用来估计发病率。我们的方法考虑了在生物标志物定义的早期疾病阶段花费的时间的不确定性。我们发现，在设计调查时未能考虑到这种不确定性可能导致发病率估计不准确和研究效力不足。我们在模拟中评估了我们的样本量方法，发现它们在各种潜在流行病中表现良好。用R实现我们的方法的代码可以在Wiley在线图书馆的生物识别网站上找到。
Understanding HIV incidence, the rate at which new infections occur in populations, is critical for tracking and surveillance of the epidemic. In this paper we derive methods for determining sample sizes for cross-sectional surveys to estimate incidence with sufficient precision. We further show how to specify sample sizes for two successive cross-sectional surveys to detect changes in incidence with adequate power. In these surveys biomarkers such as CD4 cell count, viral load, and recently developed serological assays are used to determine which individuals are in an early disease stage of infection. The total number of individuals in this stage, divided by the number of people who are uninfected, is used to approximate the incidence rate. Our methods account for uncertainty in the durations of time spent in the biomarker defined early disease stage. We find that failure to account for this uncertainty when designing surveys can lead to imprecise estimates of incidence and underpowered studies. We evaluated our sample size methods in simulations and found that they performed well in a variety of underlying epidemics. Code for implementing our methods in R is available with this paper at the Biometrics website on Wiley Online Library.