Jewell N P, Shiboski S C
Department of Statistics, University of California, Berkeley 94720.
Biometrics. 1990 Dec;46(4):1133-50.
Partner studies produce data on the infection status of partners of individuals known or assumed to be infected with the human immunodeficiency virus (HIV) after a known or estimated number of contacts. Previous studies have assumed a constant probability of transmission (infectivity) of the virus at each contact. Recently, interest has focused on the possibility of heterogeneity of infectivity across partnerships. This paper develops parametric and nonparametric procedures based on partner data in order to examine the risk of infection after a given number of contacts. Graphical methods and inference techniques are presented that allow the investigator to evaluate the constant infectivity model and consider the impact of heterogeneity of infectivity, error in measurement of the number of contacts, and regression effects of other covariates. The majority of the methods can be computationally implemented easily with use of software to fit generalized linear models. The concepts and techniques are closely related to ideas from discrete survival analysis. A data set on heterosexual transmission is used to illustrate the methods.
伴侣研究产生的数据涉及在已知或估计的接触次数后，已知或假定感染人类免疫缺陷病毒（HIV）的个体的伴侣的感染状况。以往的研究假定每次接触时病毒的传播概率（传染性）是恒定的。最近，人们的兴趣集中在不同伴侣间传染性存在异质性的可能性上。本文基于伴侣数据开发了参数和非参数程序，以便在给定接触次数后检查感染风险。文中给出了图形方法和推理技术，使研究者能够评估恒定传染性模型，并考虑传染性异质性、接触次数测量误差以及其他协变量的回归效应的影响。大多数方法通过使用软件拟合广义线性模型可以很容易地在计算上实现。这些概念和技术与离散生存分析的思想密切相关。使用一个关于异性传播的数据集来说明这些方法。
