The LIFEREG Procedure

Predicted Values

For a given set of covariates, bold x (including the intercept term), the pth quantile of the log response, y Subscript p, is given by

y Subscript p Baseline equals bold x prime bold-italic beta plus sigma u Subscript p

if no offset variable has been specified, or

y Subscript p Baseline equals bold x prime bold-italic beta plus normal o plus sigma u Subscript p

for a given value o of an offset variable, where u Subscript p is the pth quantile of the baseline distribution. The estimated quantile is computed by replacing the unknown parameters with their estimates, including any shape parameters on which the baseline distribution might depend. The estimated quantile of the original response is obtained by taking the exponential of the estimated log quantile unless the NOLOG option is specified in the preceding MODEL statement.

The following table shows how u Subscript p is computed from the baseline distribution upper F left-parenthesis u right-parenthesis:

Distribution upper F left-parenthesis u right-parenthesis u Subscript p
Exponential 1 minus exp left-parenthesis minus exp left-parenthesis u right-parenthesis right-parenthesis log left-parenthesis minus log left-parenthesis 1 minus p right-parenthesis right-parenthesis
Generalized gamma StartLayout Enlarged left-brace 1st Row 1st Column StartFraction normal upper Gamma left-parenthesis delta Superscript negative 2 Baseline comma delta Superscript negative 2 Baseline exp left-parenthesis delta u right-parenthesis right-parenthesis Over normal upper Gamma left-parenthesis delta Superscript negative 2 Baseline right-parenthesis EndFraction 2nd Column Blank 3rd Column normal i normal f delta greater-than 0 2nd Row 1st Column 1 minus StartFraction normal upper Gamma left-parenthesis delta Superscript negative 2 Baseline comma delta Superscript negative 2 Baseline exp left-parenthesis delta u right-parenthesis right-parenthesis Over normal upper Gamma left-parenthesis delta Superscript negative 2 Baseline right-parenthesis EndFraction 2nd Column Blank 3rd Column normal i normal f delta less-than 0 EndLayout upper F Superscript negative 1 Baseline left-parenthesis p right-parenthesis
Logistic 1 minus left-parenthesis 1 plus exp left-parenthesis u right-parenthesis right-parenthesis Superscript negative 1 log left-parenthesis p slash left-parenthesis 1 minus p right-parenthesis right-parenthesis
Log-logistic 1 minus left-parenthesis 1 plus exp left-parenthesis u right-parenthesis right-parenthesis Superscript negative 1 log left-parenthesis p slash left-parenthesis 1 minus p right-parenthesis right-parenthesis
Lognormal normal upper Phi left-parenthesis u right-parenthesis normal upper Phi Superscript negative 1 Baseline left-parenthesis p right-parenthesis
Normal normal upper Phi left-parenthesis u right-parenthesis normal upper Phi Superscript negative 1 Baseline left-parenthesis p right-parenthesis
Weibull 1 minus exp left-parenthesis minus exp left-parenthesis u right-parenthesis right-parenthesis log left-parenthesis minus log left-parenthesis 1 minus p right-parenthesis right-parenthesis

For the generalized gamma distribution, u Subscript p is computed numerically.

The standard errors of the quantile estimates are computed using the estimated covariance matrix of the parameter estimates and a Taylor series expansion of the quantile estimate. The standard error is computed as

normal upper S normal upper T normal upper D equals StartRoot bold z prime bold upper V bold z EndRoot

where bold upper V is the estimated covariance matrix of the parameter vector left-parenthesis bold-italic beta prime comma sigma comma delta right-parenthesis prime, and bold z is the vector

bold z equals Start 3 By 1 Matrix 1st Row  bold x 2nd Row  ModifyingAbove u With caret Subscript p Baseline 3rd Row  ModifyingAbove sigma With caret StartFraction partial-differential u Subscript p Baseline Over partial-differential delta EndFraction EndMatrix

where delta is the vector of the shape parameters. Unless the NOLOG option is specified, this standard error estimate is converted into a standard error estimate for exp left-parenthesis y Subscript p Baseline right-parenthesis as exp left-parenthesis ModifyingAbove y With caret Subscript p Baseline right-parenthesisSTD. It might be more desirable to compute confidence limits for the log response and convert them back to the original response variable than to use the standard error estimates for exp left-parenthesis y Subscript p Baseline right-parenthesis directly. See Example 76.1 for a 90% confidence interval of the response constructed by exponentiating a confidence interval for the log response.

The variable CDF is computed as

normal upper C normal upper D normal upper F Subscript i Baseline equals upper F left-parenthesis u Subscript i Baseline right-parenthesis

where the residual is defined by

u Subscript i Baseline equals left-parenthesis StartFraction y Subscript i Baseline minus bold x prime Subscript i Baseline bold b Over ModifyingAbove sigma With caret EndFraction right-parenthesis

and F is the baseline cumulative distribution function. If the data are interval-censored, then the cumulative distribution function, normal upper C normal upper D normal upper F Subscript i Baseline equals upper F left-parenthesis u Subscript i Baseline right-parenthesis, is evaluated at the lower interval endpoint.

Last updated: December 09, 2022