The PHREG Procedure

Hazard Ratios

Consider a dichotomous risk factor variable X that takes the value 1 if the risk factor is present and 0 if the risk factor is absent. The log-hazard function is given by

$\log [\lambda (t|X)] = \log [\lambda _0(t)] + \beta _1 X$

where $\lambda _0(t)$ is the baseline hazard function.

The hazard ratio $\psi$ is defined as the ratio of the hazard for those with the risk factor (X = 1) to the hazard without the risk factor (X = 0). The log of the hazard ratio is given by

$\log (\psi ) \equiv \log [\psi (X=1,X=0)] = \log [\lambda (t|X=1)] - \log [\lambda (t|X=0)] = \beta _1$

In general, the hazard ratio can be computed by exponentiating the difference of the log-hazard between any two population profiles. This is the approach taken by the HAZARDRATIO statement, so the computations are available regardless of parameterization, interactions, and nestings. However, as shown in the preceding equation for $\log (\psi )$ , hazard ratios of main effects can be computed as functions of the parameter estimates, and the remainder of this section is concerned with this methodology.

The parameter, $\beta _1$ , associated with X represents the change in the log-hazard from X = 0 to X = 1. So the hazard ratio is obtained by simply exponentiating the value of the parameter associated with the risk factor. The hazard ratio indicates how the hazard change as you change X from 0 to 1. For instance, $\psi =2$ means that the hazard when X = 1 is twice the hazard when X = 0.

Suppose the values of the dichotomous risk factor are coded as constants a and b instead of 0 and 1. The hazard when becomes $\lambda (t)\exp (a\beta _1)$ , and the hazard when becomes $\lambda (t)\exp (b\beta _1)$ . The hazard ratio corresponding to an increase in X from a to b is

$\psi = \exp [(b - a) \beta _1] = [\exp (\beta _1)]^{b-a} \equiv [\exp (\beta _1)]^ c$

Note that for any a and b such that $c=b-a=1, \psi =\exp (\beta _1)$ . So the hazard ratio can be interpreted as the change in the hazard for any increase of one unit in the corresponding risk factor. However, the change in hazard for some amount other than one unit is often of greater interest. For example, a change of one pound in body weight might be too small to be considered important, while a change of 10 pounds might be more meaningful. The hazard ratio for a change in X from a to b is estimated by raising the hazard ratio estimate for a unit change in X to the power of as shown previously.

For a polytomous risk factor, the computation of hazard ratios depends on how the risk factor is parameterized. For illustration, suppose that Cell is a risk factor with four categories: Adeno, Large, Small, and Squamous.

For the effect parameterization scheme (PARAM=EFFECT) with Squamous as the reference group, the design variables for Cell are as follows:

	Design Variables
`Cell`
Adeno	1	0	0
Large	0	1	0
Small	0	0	1
Squamous	–1	–1	–1

The log-hazard for Adeno is

$\displaystyle \log [\lambda (t\|\mbox{Adeno})]$	$\displaystyle =$	$\displaystyle \log [\lambda _0(t)] + \beta _1 (X_1=1) + \beta _2 (X_2=0) + \beta _3 (X_3=0)$
$\displaystyle$	$\displaystyle =$	$\displaystyle \lambda _0(t) + \beta _1$

The log-hazard for Squamous is

$\displaystyle \log [\lambda (t\|\mbox{Squamous})]$	$\displaystyle =$	$\displaystyle \log [\lambda _0(t)] + \beta _1 (X_1=-1) + \beta _2 (X_2=-1)+ \beta _3 (X_3=-1))$
$\displaystyle$	$\displaystyle =$	$\displaystyle \log [\lambda _0(t)] - \beta _1 - \beta _2 - \beta _3$

Therefore, the log-hazard ratio of Adeno versus Squamous

$\displaystyle \log [\psi (\mbox{Adeno},\mbox{Squamous})]$	$\displaystyle =$	$\displaystyle \log [\lambda (t\|\mbox{Adeno})] - \log [\lambda (t\|\mbox{Squamous})]$
$\displaystyle$	$\displaystyle =$	$\displaystyle 2 \beta _1 + \beta _2 + \beta _3$

For the reference cell parameterization scheme (PARAM=REF) with Squamous as the reference cell, the design variables for Cell are as follows:

	Design Variables
`Cell`
Adeno	1	0	0
Large	0	1	0
Small	0	0	1
Squamous	0	0	0

The log-hazard ratio of Adeno versus Squamous is given by

$\displaystyle \lefteqn{\log (\psi (\mbox{Adeno},\mbox{Squamous}))}$
$\displaystyle$	$\displaystyle =$	$\displaystyle \log [\lambda (t\|\mbox{Adeno})] - \log [\lambda (t\|\mbox{Squamous})]$
$\displaystyle$	$\displaystyle =$	$\displaystyle (\log [\lambda _0(t)] + \beta _1 (X_1=1) + \beta _2 (X_2=0) + \beta _3 (X_3=0)) -$
$\displaystyle$	$\displaystyle$	$\displaystyle (\log [\lambda _0(t)] + \beta _1 (X_1=0) + \beta _2 (X_2=0) + \beta _3 (X_3=0))$
$\displaystyle$	$\displaystyle =$	$\displaystyle \beta _1$

For the GLM parameterization scheme (PARAM=GLM), the design variables are as follows:

	Design Variables
`Cell`
Adeno	1	0	0	0
Large	0	1	0	0
Small	0	0	1	0
Squamous	0	0	0	1

The log-hazard ratio of Adeno versus Squamous is

$\displaystyle \lefteqn{\log (\psi (\mbox{Adeno},\mbox{Squamous}))}$
$\displaystyle$	$\displaystyle =$	$\displaystyle \log [\lambda (t\|\mbox{Adeno})] - \log [\lambda (t\|\mbox{Squamous})]$
$\displaystyle$	$\displaystyle =$	$\displaystyle \log [\lambda _0(t)] + \beta _1 (X_1=1) + \beta _2 (X_2=0) + \beta _3 (X_3=0) + \beta _4 (X_4=0)) -$
$\displaystyle$	$\displaystyle$	$\displaystyle (\log (\lambda _0(t)) + \beta _1 (X_1=0) + \beta _2 (X_2=0) + \beta _3 (X_3=0) + \beta _4(X_4=1))$
$\displaystyle$	$\displaystyle =$	$\displaystyle \beta _1 - \beta _4$

Consider Cell as the only risk factor in the Cox regression in Example 67.3. The computation of hazard ratio of Adeno versus Squamous for various parameterization schemes is tabulated in Table 67.12.

Table 67.12: Hazard Ratio Comparing Adeno to Squamous

	Parameter Estimates
PARAM=	$\hat{\beta _1}$	$\hat{\beta _2}$	$\hat{\beta _3}$	$\hat{\beta _4}$	Hazard Ratio Estimates
EFFECT	0.5772	–0.2115	0.2454		$\exp (2 \times 0.5772 -0.2115 + 0.2454) = 3.281$
REF	1.8830	0.3996	0.8565		$\exp (1.8830) =3.281$
GLM	1.8830	0.3996	0.8565	0.0000	$\exp (1.8830) = 3.281$

The fact that the log-hazard ratio ( $\log (\psi )$ ) is a linear function of the parameters enables the HAZARDRATIO statement to compute the hazard ratio of the main effect even in the presence of interactions and nest effects. The section Hazard Ratios details the estimation of the hazard ratios in a classical analysis.

To customize hazard ratios for specific units of change for a continuous risk factor, you can use the UNITS= option in a HAZARDRATIO statement to specify a list of relevant units for each explanatory variable in the model. Estimates of these customized hazard ratios are given in a separate table. Let be a confidence interval for $\log (\psi )$ . The corresponding lower and upper confidence limits for the customized hazard ratio $\exp (c\beta _ j)$ are $\exp (cL_ j)$ and $\exp (cU_ j)$ , respectively (for ), or $\exp (cU_ j)$ and $\exp (cL_ j)$ , respectively (for ).