Spatial Joint Modelling of Multivariate Longitudinal Outcomes and Cure Proportion using Latent Gaussian model with application to dataset on HIV/AIDS patients

Spatial Joint Modelling of Multivariate Longitudinal Outcomes and Cure Proportion using Latent Gaussian model with application to dataset on HIV/AIDS patients | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Joint Modelling of Multivariate Longitudinal Outcomes and Cure Proportion using Latent Gaussian model with application to dataset on HIV/AIDS patients Ekong Aniefiok Henry, Olayiwola Olaniyi Matthew, Dawodu Ganiyu Abayomi, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7979834/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Survival analysis has seen in recent times more of joint modelling of longitudinal and survival data using approximate hierarchical Bayesian method. This study modelled jointly multivariate and cure proportion with spatial variation using latent Gaussian models (LGMs). Spline specification was used to capture nonlinear trajectories of the multivariate longitudinal biomarkers in the LGM paradigm. Spatial dependence was assumed for neighbouring locations with an improper multivariate conditionally autoregressive prior on the spatial random effects, while inverse-Wishart prior was assumed for the covariance matrix of the random effects and Gaussian priors for the fixed effects. The penalised complexity prior was assumed for the Weibull shape parameters of the baseline hazard function for the event-time and binomial distribution for latent cure classification variable. Posterior distributions were evaluated using INLA. The study was applied to longitudinal and survival HIV/AIDS patients datasets. The random spatial variability was seen to be significant for both biomarkers, random intercept for BMI and cure probabilities. The full conditional distribution of latent variable gave a predicted cure proportion of 61%. The susceptible group on the hand, had a full conditional distribution of latent incidence variable predicting susceptible proportion of 39%. Flexibility of LGM for Bayesian hierarchical was demonstrated easily for multivariate joint model with spatial cure proportion. spatial cure proportion Laplace approximation multivariate shared random effect nonlinear trajectory Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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