Improving marginal hazard ratio estimation using quadratic inference functions

Hongkai Liang (), Xiaoguang Wang (), Yingwei Peng () and Yi Niu ()
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Hongkai Liang: Dalian University of Technology
Xiaoguang Wang: Dalian University of Technology
Yingwei Peng: Queen’s University
Yi Niu: Dalian University of Technology

Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, 2023, vol. 29, issue 4, No 7, 823-853

Abstract: Abstract Clustered and multivariate failure time data are commonly encountered in biomedical studies and a marginal regression approach is often employed to identify the potential risk factors of a failure. We consider a semiparametric marginal Cox proportional hazards model for right-censored survival data with potential correlation. We propose to use a quadratic inference function method based on the generalized method of moments to obtain the optimal hazard ratio estimators. The inverse of the working correlation matrix is represented by the linear combination of basis matrices in the context of the estimating equation. We investigate the asymptotic properties of the regression estimators from the proposed method. The optimality of the hazard ratio estimators is discussed. Our simulation study shows that the estimator from the quadratic inference approach is more efficient than those from existing estimating equation methods whether the working correlation structure is correctly specified or not. Finally, we apply the model and the proposed estimation method to analyze a study of tooth loss and have uncovered new insights that were previously inaccessible using existing methods.

Keywords: Marginal proportional hazards model; Quadratic inference function; Efficiency; Clustered data; Survival analysis (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10985-023-09598-4

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