A stable, multivariate extension of the log-normal survival model.

When applied to survival data from a population of cancer patients, the log-normal model provides estimates of three important parameters: cured fraction, mean log survival time, and standard deviation log survival time. In the original model, however, these parameters are unrelated to prognostic covariates. Furthermore, the original algorithm is computationally unstable and highly dependent on initial parameter estimates. We have developed an extension of the log-normal model that stabilizes computation and expresses survival parameters as functions of prognostic covariates. We have also developed an ancillary algorithm that provides reliable initial estimates.