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Activity Number: 30 - Missing Data and Measurement Error
Type: Contributed
Date/Time: Sunday, July 28, 2019 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract #306862
Title: Threshold Regression in Presence of Missing Covariate
Author(s): Tao Yang* and Ying Huang and Youyi Fong
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
Keywords: Threshold regression model; Missing covariates; Estimated likelihood; Vaccine efficacy; Nonlinear regression model

Threshold regression is plausible to model the relationship between the risk of infectious disease to the vaccine-induced surrogate biomarker response. Despite its appealing interpretation, limited research has been conducted to study the model when covariates are missing, which is due to two phase sampling design. We focus on studying the hinge threshold regression model with interaction term to better understand the association between the vaccine-induced response with the risk of disease, and adopt estimated likelihood method to tackle missing covariate issue. A reweighted iterative algorithm is proposed for parameter estimation and the asymptotic properties of the proposed estimator are derived. The finite sample properties of the estimator including both estimation and inference are shown in simulation studies and the practical application of the proposed is illustrated through the dengue data collected from dengue vaccine efficacy trial.

Authors who are presenting talks have a * after their name.

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