Use the Nonlinear platform instead of the Fit Parametric Survival platform for parametric survival models in the following instances:
• The model is nonlinear.
• You need a distribution other than Weibull, lognormal, exponential, Fréchet, loglogistic, SEV, normal, LEV, or logistic.
• You have censoring that is not the usual right, left, or interval censoring.
With the ability to estimate parameters in specified loss functions, the Nonlinear platform becomes a powerful tool for fitting maximum likelihood models. For complete information about the Nonlinear platform, see “Nonlinear Regression” in Predictive and Specialized Modeling.
To fit a nonlinear model when data are censored, you must first use the formula editor to create a parametric equation that represents a loss function adjusted for censored observations. Then use the Nonlinear platform to estimate the parameters using maximum likelihood.
The Loss Function Templates folder has templates with formulas for exponential, extreme value, loglogistic, lognormal, normal, and one-and two-parameter Weibull loss functions. To use these loss functions, copy your time and censor values into the Time and censor columns of the loss function template. To run the model, select Nonlinear and assign the loss column as the Loss variable. Because both the response model and the censor status are included in the loss function and there are no other effects, you do not need a prediction column (model variable).