Weighted Estimating Equations for Linear Regression Analysis of Clustered Failure Time Data


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Abstract

Estimation of regression parameters in linear survival models is considered in the clustered data setting. One step updates from an initial consistent estimator are proposed. The updates are based on scores that are functions of ranks of the residuals, and that incorporate weight matrices to improve efficiency. Optimal weights are approximated as the solution to a quadratic programming problem, and asymptotic relative efficiencies to various other weights computed. Except under strong dependence, simpler methods are found to be nearly as efficient as the optimal weights. The performance of several practical estimators based on exchangeable and independence working models is explored in simulations.

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