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Multiple-index varying-coefficient models for longitudinal data

Author

Listed:
  • Hongmei Lin
  • Wenchao Xu
  • Riquan Zhang
  • Jianhong Shi
  • Yuedong Wang

Abstract

In haemodialysis patients, vascular access type is of paramount importance. Although recent studies have found that central venous catheter is often associated with poor outcomes and switching to arteriovenous fistula is beneficial, studies have not fully elucidated how the effect of switching of access on outcomes changes over time for patients on dialysis and whether the effect depends on switching time. In this paper, we characterise the switching access type effect on outcomes for haemodialysis patients. This is achieved by using a new class of multiple-index varying-coefficient (MIVC) models. We develop a new estimation procedure for MIVC models based on local linear, profile least-square method and Cholesky decomposition. Monte Carlo simulation studies show excellent finite sample performance. Finally, we analyse the dialysis data using our method.

Suggested Citation

  • Hongmei Lin & Wenchao Xu & Riquan Zhang & Jianhong Shi & Yuedong Wang, 2017. "Multiple-index varying-coefficient models for longitudinal data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 1960-1978, August.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:11:p:1960-1978
    DOI: 10.1080/02664763.2016.1238052
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    References listed on IDEAS

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    1. Jianxin Pan, 2003. "On modelling mean-covariance structures in longitudinal studies," Biometrika, Biometrika Trust, vol. 90(1), pages 239-244, March.
    2. Fan, Jianqing & Huang, Tao & Li, Runze, 2007. "Analysis of Longitudinal Data With Semiparametric Estimation of Covariance Function," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 632-641, June.
    3. Huajun Ye & Jianxin Pan, 2006. "Modelling of covariance structures in generalised estimating equations for longitudinal data," Biometrika, Biometrika Trust, vol. 93(4), pages 927-941, December.
    4. Wong, Heung & Ip, Wai-cheung & Zhang, Riquan, 2008. "Varying-coefficient single-index model," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1458-1476, January.
    5. Jianhua Z. Huang, 2002. "Varying-coefficient models and basis function approximations for the analysis of repeated measurements," Biometrika, Biometrika Trust, vol. 89(1), pages 111-128, March.
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