1 research outputs found
Gradient and likelihood ratio tests in cure rate models
In some survival studies part of the population may be no longer subject to the event of interest. The called cure rate
models take this fact into account. They have been extensively studied for several authors who have proposed extensions
and applications in real lifetime data. Classic large sample tests are usually considered in these applications, especially
the likelihood ratio. Recently a new test called gradient test has been proposed. The gradient statistic shares the same
asymptotic properties with the classic likelihood ratio and does not involve knowledge of the information matrix, which
can be an advantage in survival models. Some simulation studies have been carried out to explore the behavior of the
gradient test in finite samples and compare it with the classic tests in different models. However little is known about the
properties of these large sample tests in finite sample for cure rate models. In this work we performed a simulation study
based on the promotion time model with Weibull distribution, to assess the performance of likelihood ratio and gradient
tests in finite samples. An application is presented to illustrate the results