Reviewing of Different Methods for Handling Longitudinal Count data

In this paper, we will review the methods that used to handle longitudinal data in the case of marginal models when inferences about the population average are the primary focus [1] or when future applications of the results require the expectation of the response as a function of the current covariates [7]. We will review the generalized estimating equations method (GEE), quadratic inference functions (QIF), generalized quasi likelihood (GQL) and the generalized method of moments (GMM). These methods will be reviewed by discussing its advantages and disadvantages in more details.

Joint Modeling for Longitudinal Data with Missing Values: A Bayesian Perspective on Human Intelligence

Joint modeling in longitudinal data is an interesting area of research since it predicts the outcome with covariates that are measured repeatedly over the time. However, there is no proper methodology available in literature to incorporate the joint modeling approach for count-count response data. In addition, there are several situations where longitudinal data might not be possible to collect the complete data and the Missingness may occur due to the absence of the subjects at the follow-up. In this paper, joint modelling for longitudinal count data is adopted using Bayesian Generalized Linear Mixed Model framework to understand the association between the variables. Further, an imputation method is used to handle the missing entries in the data and the efficiency of the methodology has been studied using Markov Chain Monte-Carlo (MCMC) technique. An application to the proposed methodology has been discussed and identified the suitable nutritional supplements in Bayesian perspective without eliminating the missing entries in the dataset.

Competing risks joint models using R-INLA

The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models has largely been neglected, especially from a practitioner's point of view. In the relevant works on competing risks joint models, the assumptions of a Gaussian linear longitudinal series and proportional cause-specific hazard functions, amongst others, have remained unchallenged. In this article, we provide a framework based on R-INLA to apply competing risks joint models in a unifying way such that non-Gaussian longitudinal data, spatial structures, times-dependent splines and various latent association structures, to mention a few, are all embraced in our approach. Our motivation stems from the SANAD trial which exhibits non-linear longitudinal trajectories and competing risks for failure of treatment. We also present a discrete competing risks joint model for longitudinal count data as well as a spatial competing risks joint model as specific examples.

Marginal models for longitudinal count data with dropouts

In this article, we investigate marginal models for analyzing incomplete longitudinal count data with dropouts. Specifically, we explore commonly used generalized estimating equations and weighted generalized estimating equations for fitting log-linear models to count data in the presence of monotone missing responses. A series of simulations were carried out to examine the finite-sample properties of the estimators in the presence of both correctly specified and misspecified dropout mechanisms. An application is provided using actual longitudinal survey data from the Health and Retirement Study (HRS) (HRS, 2019)