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

Hongmei Lin, Riquan Zhang, Wenchao Xu and Yuedong Wang

Computational Statistics & Data Analysis, 2017, vol. 110, issue C, 50-63

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.

Keywords: Branching curves; Haemodialysis; Hospitalization; Local linear smoothing; Local quasi-likelihood (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947316303127
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:110:y:2017:i:c:p:50-63

DOI: 10.1016/j.csda.2016.12.012

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().