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 probl ems arise in the analysis and interpretation of studies aimed at measu ring 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 partners hips or networks. In addition, the sensitive nature of questions on se xual 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 ap proaches is complicated by the complex interactions between the mechan isms generating bias and the non-independent nature of network data. U sing a two-step Monte Carlo simulation approach, we have shown that me asures of mixing patterns and the network structure that do not accoun t for missing data and non-random sampling are severely biased. Here, we use this approach to adjust raw estimates in data to incorporate th ese effects, The results suggest that the risk for transmission of STD s in empirical data is underestimated by ignoring missing data and non -random sampling.