On estimation for accelerated failure time models with small or rare event survival data.

ABSTRACT:

Background

Separation or monotone likelihood may exist in fitting process of the accelerated failure time (AFT) model using maximum likelihood approach when sample size is small and/or rate of censoring is high (rare event) or there is at least one strong covariate in the model, resulting in infinite estimates of at least one regression coefficient.

Methods

This paper investigated the properties of the maximum likelihood estimator (MLE) of the regression parameters of the AFT models for small sample and/or rare-event situation and addressed the problems by introducing a penalized likelihood approach. The penalized likelihood function and the corresponding score equation is derived by adding a penalty term to the existing likelihood function, which was originally proposed by

SUBMITTER: Alam TF 

PROVIDER: S-EPMC9188212 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature