Generalized Method of Moments for Additive Hazards Model with Clustered Dental Survival Data
针对多元生存数据，研究了基于边际加性风险模型的广义矩估计与推断方法，提出高效迭代算法降低计算负担，并通过牙科数据实例验证。
Abstract For multivariate survival data, we study the generalized method of moments (GMM) approach to estimation and inference based on the marginal additive hazards model. We propose an efficient iterative algorithm using closed‐form solutions, which dramatically reduces the computational burden. Asymptotic normality of the proposed estimators is established, and the corresponding variance–covariance matrix can be consistently estimated. Inference procedures are derived based on the asymptotic chi‐squared distribution of the GMM objective function. Simulation studies are conducted to empirically examine the finite sample performance of the proposed method, and a real data example from a dental study is used for illustration.