Quality and Reliability Methods - SAS

396 Reliability and Survival Analysis II Chapter 20
Nonlinear Parametric Survival Models
Lognormal Loss Function
Loglogistic Loss Function
Weibull Loss Function Example
This example uses the VA Lung Cancer.jmp table. Models are fit to the survival time using the Weibull,
lognormal, and exponential distributions. Model fits include a simple survival model containing only two
regressors, a more complex model with all the regressors and some covariates, and the creation of dummy
variables for the covariate Cell Type to be included in the full model.
1. Open the VA Lung Cancer.jmp sample data table.
The first model and all the loss functions have already been created as formulas in the data table. The
Model column has the following formula:
Log(:Time) - (b0 + b1 * Age + b2 * Diag Time)
Nonlinear model fitting is often sensitive to the initial values you give to the model parameters. In this
example, one way to find reasonable initial values is to first use the Nonlinear platform to fit only the
linear model. When the model converges, the solution values for the parameters become the initial
parameter values for the nonlinear model.
2. Select Analyze >Modeling > Nonlinear.
3. Select Model and click X, Predictor Formula.
4. Click OK.
5. Click Go.
The platform computes the least squares parameter estimates for this model, as shown in Figure 20.21.