Marginalized Two-Part Joint Modeling of Longitudinal Semi-Continuous Responses and Survival Data: With Application to Medical Costs

Mohadeseh Shojaei Shahrokhabadi, (Din) Ding-Geng Chen, Sayed Jamal Mirkamali, Anoshirvan Kazemnejad and Farid Zayeri
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Mohadeseh Shojaei Shahrokhabadi: Department of Statistics, University of Pretoria, Pretoria 0028, South Africa
(Din) Ding-Geng Chen: Department of Statistics, University of Pretoria, Pretoria 0028, South Africa
Sayed Jamal Mirkamali: Department of Mathematics, Faculty of Sciences, Arak University, Arak 38481-77584, Iran
Anoshirvan Kazemnejad: Department of Biostatistics, Faculty of Medical Sciences, Tarbiat Modares University, Tehran 14115111, Iran
Farid Zayeri: Proteomics Research Center and Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran 14115111, Iran

Mathematics, 2021, vol. 9, issue 20, 1-20

Abstract: 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.

Keywords: zero-inflated; right-skewed; semi-continuous; conventional two-part joint model; marginalized two-part joint model; proportional hazards model; medical costs data (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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