Abstract
We consider a specific situation of correlated data where multiple outcomes are repeatedly measured on each member of a couple. Such multivariate longitudinal data from couples may exhibit multi-faceted correlations that can be further complicated if there are polygamous partnerships. An example is data from cohort studies on human papillomavirus (HPV) transmission dynamics in heterosexual couples. HPV is a common sexually transmitted disease with 14 known oncogenic types causing anogenital cancers. The binary outcomes on the multiple types measured in couples over time may introduce inter-type, intra-couple, and temporal correlations. Simple analysis using generalized estimating equations or random effects models lacks interpretability and cannot fully use the available information. We developed a hybrid modeling strategy using Markov transition models together with pairwise composite likelihood for analyzing such data. The method can be used to identify risk factors associated with HPV transmission and persistence, estimate difference in risks between male-to-female and female-to-male HPV transmission, compare type-specific transmission risks within couples, and characterize the inter-type and intra-couple associations. Applying the method to HPV couple data collected in a Ugandan male circumcision (MC) trial, we assessed the effect of MC and the role of gender on risks of HPV transmission and persistence. Supplementary materials for this article are available online.
Highlights
Correlated data are common in clustered studies where cross-sectional correlation exists among outcomes within a cluster, or in longitudinal studies where temporal correlation exists among the repeated measurements
In the context of human papillomavirus (HPV) transmission dynamics within couples, data on multiple HPV types are often available from each partner, and the temporal correlation can translate to the scientific interest on the event of “transmission”
Motivated by the operational definition of HPV transmission, we proposed a hybrid modeling strategy that combinatorially uses the Markovian transition model and pairwise conditional likelihood method
Summary
Correlated data are common in clustered studies where cross-sectional correlation exists among outcomes within a cluster, or in longitudinal studies where temporal correlation exists among the repeated measurements. Mitchell et al (2011) proposed a discrete-time semi-Markov model that avoids the parametric assumptions in Kang and Lagakos (2007), and provides a modeling framework that can accommodate different operational definitions of HPV persistence used by different HPV researchers All these methods apply to data from an individual HPV type. Couple studies provide a unique opportunity to estimate levels of and differences between male-to-female and female-to-male HPV transmission risks. Such information is critical for cost effectiveness research on HPV prevention strategies, and will aid policy decisions about the need for vaccination of boys and young men (Palefsky, 2010; Kim and Goldie, 2009).
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