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An approximate joint model for multiple paired longitudinal outcomes and time‐to‐event data

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  • Angelo F. Elmi
  • Katherine L. Grantz
  • Paul S. Albert

Abstract

Joint modeling of multivariate paired longitudinal data and time‐to‐event data presents computational challenges that supersede full likelihood estimation due to the large dimensional random effects vector needed to capture correlation due to clustering with respect to pairs, subjects, and outcomes. We propose an alternative, computationally simpler approach to estimation of complex shared parameter models where missing data is imputed based on the Posterior Predictive Distribution from a Conditional Linear Model (CLM) approximation. Existing methods for complete data are then implemented to obtain estimates of the event time model parameters. Our method is applied to examine the effects of discordant growth in anthropometric measures of longitudinal fetal growth in twin fetuses and the timing of birth. Simulation results are presented to show that our method performs relatively well with moderate measurement errors under certain CLM approximations.

Suggested Citation

  • Angelo F. Elmi & Katherine L. Grantz & Paul S. Albert, 2018. "An approximate joint model for multiple paired longitudinal outcomes and time‐to‐event data," Biometrics, The International Biometric Society, vol. 74(3), pages 1112-1119, September.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:3:p:1112-1119
    DOI: 10.1111/biom.12862
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    References listed on IDEAS

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    1. Wen Ye & Xihong Lin & Jeremy M. G. Taylor, 2008. "Semiparametric Modeling of Longitudinal Measurements and Time-to-Event Data–A Two-Stage Regression Calibration Approach," Biometrics, The International Biometric Society, vol. 64(4), pages 1238-1246, December.
    2. Steffen Fieuws & Geert Verbeke, 2006. "Pairwise Fitting of Mixed Models for the Joint Modeling of Multivariate Longitudinal Profiles," Biometrics, The International Biometric Society, vol. 62(2), pages 424-431, June.
    3. John A. Rice & Colin O. Wu, 2001. "Nonparametric Mixed Effects Models for Unequally Sampled Noisy Curves," Biometrics, The International Biometric Society, vol. 57(1), pages 253-259, March.
    4. Joseph W. Hogan & Xihong Lin & Benjamin Herman, 2004. "Mixtures of Varying Coefficient Models for Longitudinal Data with Discrete or Continuous Nonignorable Dropout," Biometrics, The International Biometric Society, vol. 60(4), pages 854-864, December.
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