The GENMOD Procedure

Confidence Intervals for Parameters

Likelihood Ratio-Based Confidence Intervals

PROC GENMOD produces likelihood ratio-based confidence intervals, also known as profile likelihood confidence intervals, for parameter estimates for generalized linear models. These are not computed for GEE models, since there is no likelihood for this type of model. Suppose that the parameter vector is bold-italic beta equals left-bracket beta 0 comma beta 1 comma ellipsis comma beta Subscript p Baseline right-bracket prime and that you want a confidence interval for beta Subscript j. The profile likelihood function for beta Subscript j is defined as

l Superscript asterisk Baseline left-parenthesis beta Subscript j Baseline right-parenthesis equals max Underscript bold-italic beta overTilde Endscripts l left-parenthesis bold-italic beta right-parenthesis

where bold-italic beta overTilde is the vector bold-italic beta with the jth element fixed at beta Subscript j and l is the log-likelihood function. If l equals l left-parenthesis ModifyingAbove bold-italic beta With caret right-parenthesis is the log likelihood evaluated at the maximum likelihood estimate ModifyingAbove bold-italic beta With caret, then 2 left-parenthesis l minus l Superscript asterisk Baseline left-parenthesis beta Subscript j Baseline right-parenthesis right-parenthesis has a limiting chi-square distribution with one degree of freedom if beta Subscript j is the true parameter value. A left-parenthesis 1 minus alpha right-parenthesis 100 percent-sign confidence interval for beta Subscript j is

StartSet beta Subscript j Baseline colon l Superscript asterisk Baseline left-parenthesis beta Subscript j Baseline right-parenthesis greater-than-or-equal-to l 0 equals l minus 0.5 chi Subscript 1 minus alpha comma 1 Superscript 2 Baseline EndSet

where chi Subscript 1 minus alpha comma 1 Superscript 2 is the 100 left-parenthesis 1 minus alpha right-parenthesisth percentile of the chi-square distribution with one degree of freedom. The endpoints of the confidence interval can be found by solving numerically for values of beta Subscript j that satisfy equality in the preceding relation. PROC GENMOD solves this by starting at the maximum likelihood estimate of bold-italic beta. The log-likelihood function is approximated with a quadratic surface, for which an exact solution is possible. The process is iterated until convergence to an endpoint is attained. The process is repeated for the other endpoint.

Convergence is controlled by the CICONV= option in the MODEL statement. Suppose epsilon is the number specified in the CICONV= option. The default value of epsilon is 10 Superscript negative 4. Let the parameter of interest be beta Subscript j, and define bold r equals bold u Subscript j, the unit vector with a 1 in position j and 0s elsewhere. Convergence is declared on the current iteration if the following two conditions are satisfied:

StartLayout 1st Row  StartAbsoluteValue l Superscript asterisk Baseline left-parenthesis beta Subscript j Baseline right-parenthesis minus l 0 EndAbsoluteValue less-than-or-equal-to epsilon 2nd Row  left-parenthesis bold s plus lamda bold r right-parenthesis prime bold upper H Superscript negative 1 Baseline left-parenthesis bold s plus lamda bold r right-parenthesis less-than-or-equal-to epsilon EndLayout

where l Superscript asterisk Baseline left-parenthesis beta Subscript j Baseline right-parenthesis, bold s, and bold upper H are the log likelihood, the gradient, and the Hessian evaluated at the current parameter vector and lamda is a constant computed by the procedure. The first condition for convergence means that the log-likelihood function must be within epsilon of the correct value, and the second condition means that the gradient vector must be proportional to the restriction vector bold r.

When you specify the LRCI option in the MODEL statement, PROC GENMOD computes profile likelihood confidence intervals for all parameters in the model, including the scale parameter, if there is one. The interval endpoints are displayed in a table as well as the values of the remaining parameters at the solution.

Wald Confidence Intervals

You can request that PROC GENMOD produce Wald confidence intervals for the parameters. The (1 minus alpha)100% Wald confidence interval for a parameter beta is defined as

ModifyingAbove beta With caret plus-or-minus z Subscript 1 minus alpha slash 2 Baseline ModifyingAbove sigma With caret

where z Subscript p is the 100 pth percentile of the standard normal distribution, ModifyingAbove beta With caret is the parameter estimate, and ModifyingAbove sigma With caret is the estimate of its standard error.

Last updated: December 09, 2022