Wald-based and likelihood-ratio-based confidence intervals are available in the MODEL procedure for computing a confidence interval on an estimated parameter. A confidence interval on a parameter can be constructed by inverting a Wald-based or a likelihood-ratio-based test.

The approximate % Wald confidence interval for a parameter is

where is the th percentile of the standard normal distribution, is the maximum likelihood estimate of , and is the standard error estimate of .

A likelihood-ratio-based confidence interval is derived from the distribution of the generalized likelihood ratio test. The approximate confidence interval for a parameter is

where is the quantile of the with one degree of freedom, and is the log likelihood as a function of one parameter. The endpoints of a confidence interval are the zeros of the function . Computing a likelihood-ratio-based confidence interval is an iterative process. This process must be performed twice for each parameter, so the computational cost is considerable. Using a modified form of the algorithm recommended by Venzon and Moolgavkar (1988), you can determine that the cost of each endpoint computation is approximately the cost of estimating the original system.

To request confidence intervals on estimated parameters, specify the PRL= option in the FIT statement. By default, the PRL option produces 95% likelihood ratio confidence limits. The coverage of the confidence interval is controlled by the ALPHA= option in the FIT statement.

The following is an example of the use of the confidence interval options.

data exp;
   do time = 1 to 20;
      y = 35 * exp( 0.01 * time ) + 5*rannor( 123 );
   output;
   end;
run;

proc model data=exp;
   parm zo 35 b;
      dert.z = b * z;
      y=z;
   fit y init=(z=zo) / prl=both;
   test zo = 40.475437 ,/ lr;
run;

The output from the requested confidence intervals and the TEST statement are shown in Figure 18.56