The generalized moment estimation of the additive–multiplicative hazard model with auxiliary survival information

Wenpeng Shang and Xiao Wang

Computational Statistics & Data Analysis, 2017, vol. 112, issue C, 154-169

Abstract: Additive–multiplicative hazard model is a natural extension of the proportional hazard model and the additive hazard model in survival analysis. It is classical for applying the martingale estimating functions to estimate the regression parameters. However, the generalized moment method is employed to estimate the coefficients via synthesizing the auxiliary subgroup survival information. The estimators are established to be consistent and asymptotically normal. Furthermore, the method is more efficient than the famous martingale approach. In particular, these asymptotic variance–covariances are identical as the number of subgroups is equal to one. The large sample property of the Breslow estimator for the baseline cumulative hazard function is also investigated. Some extensive simulation studies are conducted to evaluate the finite-sample performances of the proposed method. A real data study is analyzed to show its practical utility.

Keywords: Additive–multiplicative hazard; Generalized moment; Loss function; Parametric estimation; Subgroup information; Survival analysis (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:112:y:2017:i:c:p:154-169

DOI: 10.1016/j.csda.2017.03.013

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