Assortativity coefficient-based estimation of population patterns of sexual mixing when cluster size is informative

作者：Young Siobhan K^*; Lyles Robert H; Kupper Lawrence L; Keys Jessica R; Martin Sandra L; Costenbader Elizabeth C

来源：Sexually Transmitted Infections, 2014, 90(4): 332-336.

DOI：10.1136/sextrans-2013-051282

Objectives Population sexual mixing patterns can be quantified using Newman's assortativity coefficient (r). Suggested methods for estimating the SE for r may lead to inappropriate statistical conclusions in situations where intracluster correlation is ignored and/or when cluster size is predictive of the response. We describe a computer-intensive, but highly accessible, within-cluster resampling approach for providing a valid large-sample estimated SE for r and an associated 95% CI.
Methods We introduce needed statistical notation and describe the within-cluster resampling approach. Sexual network data and a simulation study were employed to compare within-cluster resampling with standard methods when cluster size is informative.
Results For the analysis of network data when cluster size is informative, the simulation study demonstrates that within-cluster resampling produces valid statistical inferences about Newman's assortativity coefficient, a popular statistic used to quantify the strength of mixing patterns. In contrast, commonly used methods are biased with attendant extremely poor CI coverage. Within-cluster resampling is recommended when cluster size is informative and/or when there is within-cluster response correlation.
Conclusions Within-cluster resampling is recommended for providing valid statistical inferences when applying Newman's assortativity coefficient r to network data.

• 出版日期2014-6