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Estimating time-varying treatment switching effects via local linear smoothing and quasi-likelihood

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  • Lin, Hongmei
  • Zhang, Riquan
  • Xu, Wenchao
  • Wang, Yuedong

Abstract

Vascular access complications have been the major cause of excessive morbidity and mortality in the dialysis population. They also account for a large portion of hospitalization for dialysis patients and are a main contributor to the high dialysis care cost. Despite the Fistula First Initiative, the majority of patients initiate dialysis with a central venous catheter which is associated with poor outcomes. In this paper we investigate whether switching from a central venous catheter to an arteriovenous fistula sooner is associated with smaller hospitalization rate. We propose a flexible model for time-varying switching effect while accounting for trend over calendar time, trend over time on dialysis and time-varying effects of covariates. We model all unknown functions nonparametrically using local linear smoothers and estimate them using weighted local quasi-likelihood. We show that the proposed estimators have the desirable large-sample properties and excellent performance in simulations. Application of the proposed method to a real data set indicates that hospitalization rate is smaller when patients switch from a central venous catheter to an arteriovenous fistula sooner. The proposed methods are general which are applicable to other situations with treatment switching.

Suggested Citation

  • Lin, Hongmei & Zhang, Riquan & Xu, Wenchao & Wang, Yuedong, 2017. "Estimating time-varying treatment switching effects via local linear smoothing and quasi-likelihood," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 50-63.
    • Handle: RePEc:eee:csdana:v:110:y:2017:i:c:p:50-63
    DOI: 10.1016/j.csda.2016.12.012
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    References listed on IDEAS

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    1. Pollard, David, 1991. "Asymptotics for Least Absolute Deviation Regression Estimators," Econometric Theory, Cambridge University Press, vol. 7(2), pages 186-199, June.
    2. Jason P. Estes & Danh V. Nguyen & Lorien S. Dalrymple & Yi Mu & Damla Şentürk, 2014. "Cardiovascular event risk dynamics over time in older patients on dialysis: A generalized multiple-index varying coefficient model approach," Biometrics, The International Biometric Society, vol. 70(3), pages 751-761, September.
    3. Ma, Ping & Zhong, Wenxuan, 2008. "Penalized Clustering of Large-Scale Functional Data With Multiple Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 625-636, June.
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