Estimation of regression parameters and the hazard function in transformed linear survival models.
An estimator of the regression parameters in a semiparametric transformed linear survival model is examined. This estimator consists of a single Newton-like update of the solution to a rank-based estimating equation from an initial consistent estimator. An automated penalized likelihood algorithm is proposed for estimating the optimal weight function for the estimating equations and the error hazard function that is needed in the variance estimator. In simulations, the estimated optimal weights are found to give reasonably efficient estimators of the regression parameters, and the variance estimators are found to perform well. The methodology is applied to an analysis of prognostic factors in non-Hodgkin's lymphoma.
Gray
R J
RJ
Department of Biostatistical Science, Dana Farber Cancer Institute and Harvard School of Public Health, Boston, Massachusetts 02115, USA. gray@jimmy.harvard.edu
D000704:Analysis of Variance; D006801:Humans; D016013:Likelihood Functions; D008228:Lymphoma, Non-Hodgkin / Q000401:mortality / Q000628:therapy; D011379:Prognosis; D016016:Proportional Hazards Models; D012044:Regression Analysis; D015996:Survival Rate; D017211:Treatment Failure