Statistical Analysis of HIV Infectivity Based on Partner Studies

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 a...

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Bibliographic Details
Published in:Biometrics 1990-12, Vol.46 (4), p.1133-1150
Main Authors: Jewell, Nicholas P., Shiboski, Stephen C.
Format: Article
Language:English
Subjects:
AIDS/HIV
Biometry
Collaboration
Condoms
Disease transmission
Female
HIV
HIV Infections - transmission
HIV Seropositivity
Humans
Infections
Male
Maximum likelihood estimation
Modeling
Models, Statistical
Probability
Random errors
Regression coefficients
Sexual Behavior
Sexually Transmitted Diseases, Viral - transmission
Transcriptional regulatory elements
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Summary: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.
ISSN:0006-341X
1541-0420
DOI:10.2307/2532454