A varying-coefficient partially linear transformation model for length-biased data with an application to HIV vaccine studies

Wan Alan T. K., Zhao Wei (), Gilbert Peter and Zhou Yong
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Wan Alan T. K.: Department of Biostatistics, Department of Management Sciences and School of Data Science, City University of Hong Kong, Hong Kong, China
Zhao Wei: Zhongtai Securities Institute for Financial Studies, Shandong University, Jinan 250100, China
Gilbert Peter: Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
Zhou Yong: Faculty of Economics and Management, East China Normal University, Shanghai 200241, China

The International Journal of Biostatistics, 2023, vol. 19, issue 1, 131-162

Abstract: Prevalent cohort studies in medical research often give rise to length-biased survival data that require special treatments. The recently proposed varying-coefficient partially linear transformation (VCPLT) model has the virtue of providing a more dynamic content of the effects of the covariates on survival times than the well-known partially linear transformation (PLT) model by allowing flexible interactions between the covariates. However, no existing analysis of the VCPLT model has considered length-biased sampling. In this paper, we consider the VCPLT model when the data are length-biased and right censored, thereby extending the reach of this flexible and powerful tool. We develop a martingale estimating function-based approach to the estimation of this model, provide theoretical underpinnings, evaluate finite sample performance via simulations, and showcase its practical appeal via an empirical application using data from two HIV vaccine clinical trials conducted by the U.S. National Institute of Allergy and Infectious Diseases.

Keywords: HVTN; length-biasedness; martingale; right-censoring (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2021-0057

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