Measuring sexual partner networks for transmission of sexually transmitted diseases
Measuring sexual partner networks for transmission of sexually transmitted diseases
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DOI：
10.1111/1467-985x.00101
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发表时间：
1998-01-01
影响因子：
2
通讯作者：
Garnett, GP
中科院分区：
文献类型：
--
作者：
Ghani, AC;Garnett, GP
混合性行为的模式和性伙伴网络是所有性传播疾病（性病），包括人体免疫机能丧失病毒传播的重要决定因素。新的统计问题出现在分析和解释的研究，旨在衡量模式的性混合和性伙伴网络。从随机抽样的个人中得出的混合模式和网络结构的样本本身并不是衡量伙伴关系或网络的随机样本。此外，关于性活动的问题的敏感性将导致引入无反应偏差，这在估计网络结构时可能是不可解释的。通过使用标准统计方法调整这些偏差的估计是复杂的，因为产生偏差的机制和网络数据的非独立性之间存在复杂的相互作用。使用两步蒙特卡罗模拟方法，我们已经表明，混合模式和网络结构的措施，不考虑缺失数据和非随机抽样严重偏差。在这里，我们使用这种方法来调整原始估计的数据，以纳入这些影响，结果表明，在经验数据中的性病传播的风险被低估，忽略缺失数据和非随机抽样。
Patterns of sexual mixing and the sexual partner network are important determinants of the spread of all sexually transmitted diseases (STDs), including the human immunodeficiency virus. Novel statistical problems arise in the analysis and interpretation of studies aimed at measuring patterns of sexual mixing and sexual partner networks. Samples of mixing patterns and network structures derived from randomly sampling individuals are not themselves random samples of measures of partnerships or networks. In addition, the sensitive nature of questions on sexual activity will result in the introduction of non-response biases, which in estimating network structures are likely to be non-ignorable. Adjusting estimates for these biases by using standard statistical approaches is complicated by the complex interactions between the mechanisms generating bias and the non-independent nature of network data. Using a two-step Monte Carlo simulation approach, we have shown that measures of mixing patterns and the network structure that do not account for missing data and non-random sampling are severely biased. Here, we use this approach to adjust raw estimates in data to incorporate these effects, The results suggest that the risk for transmission of STDs in empirical data is underestimated by ignoring missing data and non-random sampling.