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

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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
    College of Health Solutions, Arizona State University, Phoenix, AZ 85004, USA)

  • 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)

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.

Suggested Citation

  • Mohadeseh Shojaei Shahrokhabadi & (Din) Ding-Geng Chen & Sayed Jamal Mirkamali & Anoshirvan Kazemnejad & Farid Zayeri, 2021. "Marginalized Two-Part Joint Modeling of Longitudinal Semi-Continuous Responses and Survival Data: With Application to Medical Costs," Mathematics, MDPI, vol. 9(20), pages 1-20, October.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:20:p:2603-:d:657615
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    References listed on IDEAS

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    1. Liu, Lei & Strawderman, Robert L. & Cowen, Mark E. & Shih, Ya-Chen T., 2010. "A flexible two-part random effects model for correlated medical costs," Journal of Health Economics, Elsevier, vol. 29(1), pages 110-123, January.
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