Measuring sexual partner networks for transmission of sexually transmitted diseases

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.