With parametric cure models, we can express survival parameters (e.g. cured
fraction, location and scale parameters) as functions of covariates. These
models can measure survival from a specific disease process, either by exa
mining deaths due to the cause under study (cause-specific survival), or by
comparing all deaths to those in a matched control population (relative su
rvival). We present a binomial maximum likelihood algorithm to be used for
actuarial data, where follow-up times are grouped into specific intervals.
Our algorithm provides simultaneous maximum likelihood estimates for all th
e parameters of a cure model and can be used for cause-specific or relative
survival analysis with a variety of survival distributions. Current softwa
re does not provide the flexibility of this unified approach. (C) 2000 Else
vier Science Ireland Ltd. All rights reserved.