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The structures of sexual partner networks are important in determining patterns of transmission of STDs including HIV. Empirical data on sexual partnerships and sexual partner networks collected through sampling individuals are a non-random sample of partnerships and network structures even if individuals are sampled randomly. This has the potential to bias estimates of measures describing the sexual partner network. In addition, biases may be introduced through non-response and missing data. Using Monte Carlo simulation, we investigate the biases that are introduced in estimates measures of the sexual partner network through three common sampling methods. The results indicate that substantial systematic biases are introduced. The direction and magnitude of these biases suggest that, by ignoring them, the risk for the establishment and persistence of infection in a population may be underestimated.

Original publication

DOI

10.1002/(sici)1097-0258(19980930)17:18<2079::aid-sim902>3.0.co;2-h

Type

Journal article

Journal

Statistics in medicine

Publication Date

09/1998

Volume

17

Pages

2079 - 2097

Addresses

Wellcome Trust Centre for the Epidemiology of Infectious Disease, Department of Zoology, University of Oxford, U.K. azra.ghani@zoo.ox.ac.uk

Keywords

Humans, Gonorrhea, Sexually Transmitted Diseases, Contact Tracing, Data Collection, Models, Statistical, Monte Carlo Method, Probability, Stochastic Processes, Sampling Studies, Bias (Epidemiology), Sexual Partners, Female, Male