TY - JOUR T1 - A monotone single index model for missing-at-random longitudinal proportion data JF - bioRxiv DO - 10.1101/2022.01.20.477170 SP - 2022.01.20.477170 AU - Satwik Acharyya AU - Debdeep Pati AU - Dipankar Bandyopadhyay AU - Shumei Sun Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/01/22/2022.01.20.477170.abstract N2 - Beta distributions are commonly used to model proportion valued response variables, commonly encountered in longitudinal studies. In this article, we develop semi-parametric Beta regression models for proportion valued responses, where the aggregate covariate effect is summarized and flexibly modeled, using a interpretable monotone time-varying single index transform of a linear combination of the potential covariates. We utilize the potential of single index models, which are effective dimension reduction tools and accommodate link function misspecification in generalized linear mixed models. Our Bayesian methodology incorporates the missing-at-random feature of the proportion response, and utilize Hamiltonian Monte Carlo sampling to conduct inference. We explore finite-sample frequentist properties of our estimates, and assess the robustness via detailed simulation studies. Finally, we illustrate our methodology via application to a motivating longitudinal dataset on obesity research recording proportion body fat.Competing Interest StatementThe authors have declared no competing interest. ER -