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In this paper, we develop a new estimation procedure based on the non-linearconjugate gradient (NCG) algorithm for the Box-Cox transformation cure ratemodel. We compare the performance of the NCG algorithm with the well-knownexpectation maximization (EM) algorithm through a simulation study and show theadvantages of the NCG algorithm over the EM algorithm. In particular, we showthat the NCG algorithm allows simultaneous maximization of all model parameterswhen the likelihood surface is flat with respect to a Box-Cox model parameter.This is a big advantage over the EM algorithm, where a profile likelihoodapproach has been proposed in the literature that may not provide satisfactoryresults. We finally use the NCG algorithm to analyze a well-known melanoma dataand show that it results in a better fit.

A New Estimation Algorithm for Box-Cox Transformation Cure Rate Model and Comparison With EM Algorithm