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Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes

Author

Listed:
  • Khurshid Alam

    (Case Western Reserve University)

  • Arnab Maity

    (NC State University)

  • Sanjoy K. Sinha

    (Carleton University)

  • Dimitris Rizopoulos

    (Erasmus University Medical Center)

  • Abdus Sattar

    (Case Western Reserve University)

Abstract

In this paper, we propose an innovative method for jointly analyzing survival data and longitudinally measured continuous and ordinal data. We use a random effects accelerated failure time model for survival outcomes, a linear mixed model for continuous longitudinal outcomes and a proportional odds mixed model for ordinal longitudinal outcomes, where these outcome processes are linked through a set of association parameters. A primary objective of this study is to examine the effects of association parameters on the estimators of joint models. The model parameters are estimated by the method of maximum likelihood. The finite-sample properties of the estimators are studied using Monte Carlo simulations. The empirical study suggests that the degree of association among the outcome processes influences the bias, efficiency, and coverage probability of the estimators. Our proposed joint model estimators are approximately unbiased and produce smaller mean squared errors as compared to the estimators obtained from separate models. This work is motivated by a large multicenter study, referred to as the Genetic and Inflammatory Markers of Sepsis (GenIMS) study. We apply our proposed method to the GenIMS data analysis.

Suggested Citation

  • Khurshid Alam & Arnab Maity & Sanjoy K. Sinha & Dimitris Rizopoulos & Abdus Sattar, 2021. "Joint modeling of longitudinal continuous, longitudinal ordinal, and time-to-event outcomes," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 27(1), pages 64-90, January.
    • Handle: RePEc:spr:lifeda:v:27:y:2021:i:1:d:10.1007_s10985-020-09511-3
    DOI: 10.1007/s10985-020-09511-3
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    References listed on IDEAS

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    1. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    3. Sungduk Kim & Paul S. Albert, 2016. "A class of joint models for multivariate longitudinal measurements and a binary event," Biometrics, The International Biometric Society, vol. 72(3), pages 917-925, September.
    4. Dimitris Rizopoulos & Geert Verbeke & Emmanuel Lesaffre & Yves Vanrenterghem, 2008. "A Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros," Biometrics, The International Biometric Society, vol. 64(2), pages 611-619, June.
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    Cited by:

    1. Murray, James & Philipson, Pete, 2023. "Fast estimation for generalised multivariate joint models using an approximate EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 187(C).
    2. Murray, James & Philipson, Pete, 2022. "A fast approximate EM algorithm for joint models of survival and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).

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