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Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics.

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 which 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 utilize 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.

SUBMITTER: Kong X

PROVIDER: S-EPMC4505367 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature

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Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics.

Kong Xiangrong X Wang Mei-Cheng MC Gray Ronald R

Journal of the American Statistical Association 20150601 510

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 which 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 can ...[more]

PMID: 26195849

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Project description:IntroductionHuman papillomavirus (HPV) is a causal agent of malignancies including cervical, vulvar, vaginal, penile, anal and oropharyngeal cancer, as well as benign conditions such as anogenital warts. HPV vaccination protects individuals against infections with the target HPV types and their clinical outcomes. However, little is known about the protection an immunised individual confers to their sexual partner or its impact on HPV transmission dynamics. In this context, the Transmission Reduction and Prevention with HPV vaccination (TRAP-HPV) study was designed to determine the efficacy of an HPV vaccine in reducing transmission of genital and oral HPV infection in sexual partners of vaccinated individuals.Methods and analysisThe TRAP-HPV study is an ongoing randomised controlled trial among heterosexual couples living in Montreal, Canada. Sexually active couples, aged between 18 and 45 years, who have been in a relationship no longer than 6 months are considered eligible. Participants are independently randomised to receive either the intervention HPV vaccine, Gardasil 9, or a placebo hepatitis A vaccine, Avaxim, creating four vaccination groups among couples: intervention-intervention, intervention-placebo, placebo-intervention and the placebo-placebo. Participants provide genital (vaginal/penile) and oral samples at baseline and five follow-up visits over a 1-year duration. Linear Array HPV genotyping is used to detect 36 HPV types. Cox proportional hazard regression models will be used to estimate the effect of vaccination on HPV transmission.Ethics and disseminationThe TRAP-HPV study received ethical approval by institutional review boards McGill University, Concordia University and Centre Hospitalier de l'Université de Montréal. Before enrolment, all participants provide informed written consent. Results will be published in peer-reviewed journals and presented at national and international conferences. The generated empirical evidence could be used in mathematical models of vaccination to inform policymakers in Canada and elsewhere.Trial registration numberNCT01824537.

Project description:BACKGROUND : In spite of the global reduction of 21% in malaria incidence between 2010 and 2015, the disease still threatens many lives of children and pregnant mothers in African countries. A correct assessment and evaluation of the impact of malaria control strategies still remains quintessential in order to eliminate the disease and its burden. Malaria follow-up studies typically involve routine visits at pre-scheduled time points and/or clinical visits whenever individuals experience malaria-like symptoms. In the latter case, infection triggers outcome assessment, thereby leading to outcome-dependent sampling (ODS). Commonly used methods to analyze such longitudinal data ignore ODS and potentially lead to biased estimates of malaria-specific transmission parameters, hence, inducing an incorrect assessment and evaluation of malaria control strategies. METHODS : In this paper, a new method is proposed to handle ODS by use of a joint model for the longitudinal binary outcome measured at routine visits and the clinical event times. The methodology is applied to malaria parasitaemia data from a cohort of [Formula: see text] Ugandan children aged 0.5-10 years from 3 regions (Walukuba-300 children, Kihihi-355 children and Nagongera-333 children) with varying transmission intensities (entomological inoculation rate equal to 2.8, 32 and 310 infectious bites per unit year, respectively) collected between 2011-2014. RESULTS : The results indicate that malaria parasite prevalence and force of infection (FOI) increase with age in the region of high malaria intensity with highest FOI in age group 5-10 years. For the region of medium intensity, the prevalence slightly increases with age and the FOI for the routine process is highest in age group 5-10 years, yet for the clinical infections, the FOI gradually decreases with increasing age. For the region with low intensity, both the prevalence and FOI peak at the age of 1 year after which the former remains constant with age yet the latter suddenly decreases with age for the clinically observed infections. CONCLUSION : Malaria parasite prevalence and FOI increase with age in the region of high malaria intensity. In all study sites, both the prevalence and FOI are highest among previously asymptomatic children and lowest among their symptomatic counterparts. Using a simulation study inspired by the malaria data at hand, the proposed methodology shows to have the smallest bias, especially when consecutive positive malaria parasitaemia presence results within a time period of 35 days were considered to be due to the same infection.

Project description:It is important to characterize the temporal trajectories of disease-related biomarkers in order to monitor progression and identify potential points of intervention. These are especially important for neurodegenerative diseases, as therapeutic intervention is most likely to be effective in the preclinical disease stages prior to significant neuronal damage. Neuroimaging allows for the measurement of structural, functional, and metabolic integrity of the brain at the level of voxels, whose volumes are on the order of mm(3). These voxelwise measurements provide a rich collection of disease indicators. Longitudinal neuroimaging studies enable the analysis of changes in these voxelwise measures. However, commonly used longitudinal analysis approaches, such as linear mixed effects models, do not account for the fact that individuals enter a study at various disease stages and progress at different rates, and generally consider each voxelwise measure independently. We propose a multivariate nonlinear mixed effects model for estimating the trajectories of voxelwise neuroimaging biomarkers from longitudinal data that accounts for such differences across individuals. The method involves the prediction of a progression score for each visit based on a collective analysis of voxelwise biomarker data within an expectation-maximization framework that efficiently handles large amounts of measurements and variable number of visits per individual, and accounts for spatial correlations among voxels. This score allows individuals with similar progressions to be aligned and analyzed together, which enables the construction of a trajectory of brain changes as a function of an underlying progression or disease stage. We apply our method to studying cortical β-amyloid deposition, a hallmark of preclinical Alzheimer's disease, as measured using positron emission tomography. Results on 104 individuals with a total of 300 visits suggest that precuneus is the earliest cortical region to accumulate amyloid, closely followed by the cingulate and frontal cortices, then by the lateral parietal cortex. The extracted progression scores reveal a pattern similar to mean cortical distribution volume ratio (DVR), an index of global brain amyloid levels. The proposed method can be applied to other types of longitudinal imaging data, including metabolism, blood flow, tau, and structural imaging-derived measures, to extract individualized summary scores indicating disease progression and to provide voxelwise trajectories that can be compared between brain regions.

Dataset Information

Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics.

Publications

Analysis of longitudinal multivariate outcome data from couples cohort studies: application to HPV transmission dynamics.

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