A modified score function for monotone likelihood in promotion time cure rate models

Abstract

Survival models that incorporate a cure fraction provide a flexible framework for jointly modeling the cure and the survival distributions. However, when the data comprise a high proportion of censored observations or highly unbalanced binary covariates, maximum likelihood estimation may become unstable, leading to parameter estimates that diverge to infinity. This phenomenon, commonly referred to in the literature as monotone likelihood, compromises statistical inference by precluding the existence of finite maximum likelihood estimates. Specifically, the likelihood function increases monotonically along certain directions in the parameter space, so no finite maximizer exists. To the best of our knowledge, the monotone likelihood problem has received little or no attention in the context involving the promotion time model. This paper addresses this gap by proposing a modified score function based on Firth's bias-reduction method, which adjusts the estimation procedure to ensure finite and stable parameter estimates. The performance of the proposed approach is evaluated through extensive Monte Carlo simulation studies. An application to a real dataset further demonstrates its practical advantages, showing that the mitosis factor, an established prognostic marker, becomes statistically significant under the proposed methodology.