This article aims to joint modeling of longitudinal CD4 cells count and time to death in HIV patients based on the AFT model. The modeling of the longitudinal count response, a GLME model under the family of PSD, was used. In contrast, for the TTE data, the parametric AFT model under the Weibull distribution was investigated. These two responses are linked through random effects correlated with the normal distribution. The longitudinal and survival data are then assumed independent, given the latent linking process and any available covariates. Considering excess zeros for two responses and right censoring, presented a joint model that has not yet been investigated by other researchers. The parameters were also estimated using MCMC methods.

One of the most frequently encountered longitudinal studies issues is data with losing the appointments or getting censoring. In such cases, all of the subjects do not have the same set of observation times. The missingness in the analysis of longitudinal discrete and continuous mixed data is also common, and missing may occur in one or both responses. Failure to pay attention to the cause of the missing (the mechanism of the missingness) leads to unbiased estimates and inferences. Therefore, in this paper, we investigate the mechanism of nonignorable missing in set-inflated continuous and zero-inflation power series, as well as the continuous and k-inflated ordinal mixed responses. A full likelihood-based approach is used to obtain the maximum likelihood estimates of the parameters of the models. In order to assess the performance of the models, some simulation studies are performed. Two applications of our models are illustrated for the American's Changing Lives  survey, and the Peabody Individual Achievement Test  data set.