2024 : 6 : 16
Sayed Jamal Mirkamali

Sayed Jamal Mirkamali

Academic rank: Assistant Professor
ORCID: https://orcid.org/0000-0001-7513-4423
Education: PhD.
ScopusId: 35739053200
Faculty: Science
Address: Arak University


Marginalized Two-Part Joint Modeling of Longitudinal Semi-Continuous Responses and Survival Data: With Application to Medical Costs
zero-inflated; right-skewed; semi-continuous; conventional two-part joint model; marginalized two-part joint model; proportional hazards model; medical costs data
Journal (MDPI) Mathematics
Researchers Mohadeseh Shojaei Shahrokhabadi ، Ding-Geng Chen ، Sayed Jamal Mirkamali ، Anoushirvan Kazemnejad ، Farid Zayeri


Non-negative continuous outcomes with a substantial number of zero values and incomplete longitudinal follow-up are quite common in medical costs data. It is thus critical to incorporate the potential dependence of survival status and longitudinal medical costs in joint modeling, where censorship is death-related. Despite the wide use of conventional two-part joint models (CTJMs) to capture zero-inflation, they are limited to conditional interpretations of the regression coefficients in the model’s continuous part. In this paper, we propose a marginalized two-part joint model (MTJM) to jointly analyze semi-continuous longitudinal costs data and survival data. We compare it to the conventional two-part joint model (CTJM) for handling marginal inferences about covariate effects on average costs. We conducted a series of simulation studies to evaluate the superior performance of the proposed MTJM over the CTJM. To illustrate the applicability of the MTJM, we applied the model to a set of real electronic health record (EHR) data recently collected in Iran. We found that the MTJM yielded a smaller standard error, root-mean-square error of estimates, and AIC value, with unbiased parameter estimates. With this MTJM, we identified a significant positive correlation between costs and survival, which was consistent with the simulation results.