Abstract
Analyses of viral genetic linkage can provide insight into HIV transmission dynamics and the impact of prevention interventions. For example, such analyses have the potential to determine whether recently-infected individuals have acquired viruses circulating within or outside a given community. In addition, they have the potential to identify characteristics of chronically infected individuals that make their viruses likely to cluster with others circulating within a community. Such clustering can be related to the potential of such individuals to contribute to the spread of the virus, either directly through transmission to their partners or indirectly through further spread of HIV from those partners. Assessment of the extent to which individual (incident or prevalent) viruses are clustered within a community will be biased if only a subset of subjects are observed, especially if that subset is not representative of the entire HIV infected population. To address this concern, we develop a multiple imputation framework in which missing sequences are imputed based on a model for the diversification of viral genomes. The imputation method decreases the bias in clustering that arises from informative missingness. Data from a household survey conducted in a village in Botswana are used to illustrate these methods. We demonstrate that the multiple imputation approach reduces bias in the overall proportion of clustering due to the presence of missing observations.
Highlights
Targeting HIV prevention interventions to high-risk groups [1] can be aided by analyses of HIV viral genetic linkage
Missing data may arise by happenstance or by design; consideration of the potential for bias in estimation of probabilities of linkage is especially important when the pattern of missingness may be related to characteristics of observed and missing observations—a pattern is that is referred to as informative missingness [24, 25]
We propose methods to adjust for the presence of informatively missing data in viral genetic linkage analysis
Summary
Targeting HIV prevention interventions to high-risk groups [1] can be aided by analyses of HIV viral genetic linkage. Numerous studies have already demonstrated the use of viral sequences from HIV-infected participants to investigate patterns of phylogenetic linkage, which provides information about patterns of HIV transmission dynamics [2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22]. Test statistics can help determine whether certain demographic groups are more likely to be clustered, which aids in understanding HIV transmission patterns—especially if sampled viral sequences include new infections. This paper investigates sensitivity of estimation for incomplete data, and the extent to which the proposed multiple imputation approach reduces bias in viral linkage estimates
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
