The PHREG Procedure

Robust Sandwich Variance Estimate

For the ith subject, $i=1,\ldots ,n$ , let , , and $\bZ _ i(t)$ be the observed time, weight, and the covariate vector at time t, respectively. Let $\Delta _ i$ be the event indicator and let $Y_ i(t)=I(X_ i\geq t)$ . Let

$S^{(r)}(\bbeta ,t) = \sum _{j=1}^ n w_ jY_ j(t) \mr {e}^{\bbeta \bZ _ j(t)} \bZ _ j^{\bigotimes r}(t), r=0,1$

Let $\bar{\bZ }(\bbeta ,t) = \frac{S^{(1)}(\bbeta ,t)}{S^{(0)}(\bbeta ,t)}$ . The score residual for the ith subject is

$\bL _ i(\bbeta ) = \Delta _ i\biggl \{ \bZ _ i(X_ i) - \bar{\bZ }(\beta ,X_ i)\biggr \} - \sum _{j=1}^ n \Delta _ j \frac{w_ jY_ i(X_ j)\mr {e}^{\bbeta \bZ _ i(X_ j)}}{S^{(0)}(\bbeta ,X_ j)}\biggl \{ \bZ _ i(X_ j) - \bar{\bZ }(\bbeta ,X_ j)\biggr \}$

For TIES=EFRON, the computation of the score residuals is modified to comply with the Efron partial likelihood. See the section Residuals for more information.

The robust sandwich variance estimate of $\hat{\bbeta }$ derived by Binder (1992), who incorporated weights into the analysis, is

$\hat{\bV }_ s(\hat{\bbeta }) = \mc {I}^{-1}(\hat{\bbeta }) \biggl [\sum _{j=1}^ n (w_ j\bL _ j(\hat{\bbeta }))^{\bigotimes 2} \biggr ] \mc {I}^{-1}(\hat{\bbeta })$

where $\mc {I}(\hat{\bbeta })$ is the observed information matrix, and $\mb {a}^{\bigotimes 2}=\mb {a} \mb {a}’$ . Note that when $w_ i \equiv 1$ ,

$\hat{\bV }_ s(\hat{\bbeta }) = \bD ’\bD$

where $\bD$ is the matrix of DFBETA residuals. This robust variance estimate was proposed by Lin and Wei (1989) and Reid and Crèpeau (1985).