The ICPHREG Procedure

MODEL Statement

  • MODEL (t1, t2)= effects </ options>;

The MODEL statement identifies the variables to be used as the failure-time variables and the explanatory effects, including covariates, main effects, interactions, nested effects. For more information, see the section Specification of Effects in Chapter 45: The GLM Procedure.

The MODEL syntax specifies two variables, t1 and t2, that contain values of the endpoints of the censoring interval. Only nonnegative values are accepted. If the two values are the same (and not missing), it is assumed that there is no censoring and the actual response value is observed. If the lower value is missing, then the upper value is used as a left-censored value. If the upper value is missing, then the lower value is used as a right-censored value. If both values are present and the lower value is less than the upper value, it is assumed that the values specify a censoring interval. If the lower value is greater than the upper value or both values are missing, then the observation is not used in the analysis.

The following table summarizes the ways of specifying censoring.

Lower Value


Upper Value





Not missing


Not missing




No censoring

Not missing


Not missing


Lower < upper


Censoring interval



Not missing


Upper used as left-


censoring value

Not missing




Lower used as right-


censoring value

Not missing


Not missing


Lower > upper


Observation not used





Observation not used

Table 51.5 summarizes the options that you can specify in the MODEL statement.

Table 51.5: MODEL Statement Options



Model Specification Options


Specifies the confidence level


Specifies the functional form for the baseline function


Suppresses polishing of parameter estimates of the hazard function


Specifies an offset variable to be added to the linear predictor


Requests parameterization of the hazard function in the original scale or in log scale

Output Options


Displays the estimated correlation matrix


Displays the estimated covariance matrix


specifies the level for the confidence intervals for parameters. The value must be between 0 and 1. By default, ALPHA=0.05.


displays the estimated correlation matrix of the parameter estimates.


displays the estimated covariance matrix of the parameter estimates.


specifies a functional form for the baseline function. You can specify one of the following baseline-types:

PCH (<NINTERVAL=number>, <INTERVALS=(numeric-list)>)
PIECEWISE (<NINTERVAL=number>, <INTERVALS=(numeric-list)>)
PCBH (<NINTERVAL=number>, <INTERVALS=(numeric-list)>)

partitions the time scale into disjoint intervals and assumes the baseline hazard function is piecewise constant within intervals. The parameters are the piecewise constant values of the baseline hazard functions and are named Haz1, Haz2, $\ldots $, and so on. If HAZARDSCALE=LOGHAZ is specified, the names are LogHaz1, LogHaz2, $\ldots $, and so on.

You can specify one of the following two options to control how to partition the time axis into intervals of constant baseline hazards:


specifies the number of intervals that have a constant hazard rate in each interval. PROC ICPHREG partitions the time axis into the number of intervals so that each interval contains an approximately equal number of unique boundary values and imputed middle points.


specifies a list of numbers that partition the time axis into disjoint intervals that have constant hazard rate in each interval. For example, INTERVALS=(100, 150, 200, 250, 300) specifies a model that has a constant hazard in the intervals [0,100), [100,150), [150,200), [200,250), [250,300), and [300,$\infty $).

If you specify neither NINTERVAL= nor INTERVAL= , NINTERVAL=5 by default.

SPLINES (<DF=number>)

models the baseline cumulative hazard function by cubic splines (Royston and Parmar, 2002). The parameters are the spline coefficients and are named Coef1, Coef2, $\ldots $, and so on.

You can specify the degrees of freedom in the DF=number option, where number must be an integer. The number of knots equals number plus one. The actual positions of the knots are determined from an imputed data set as follows. First, PROC ICPHREG imputes a middle point for each observation in the input data set that is not right-censored. Then, it sorts these imputed times and the input boundary values in increasing order and selects only unique values. PROC ICPHREG places the terminal knots at the minimum and maximum of this sequence and chooses the interval knots by using the same method it uses to choose the break points for the piecewise constant model. For more information, see the section Choosing Break Points.

By default, DF=2.

If you do not specify the BASEHAZ= option, the ICPHREG procedure fits a piecewise constant model as if NINTERVAL=5.


suppresses polishing of parameter estimates of the baseline function. Occasionally, the parameter estimates of the baseline function can reach the default optimization lower bounds. This might indicate that the model is overparameterized. By default, the ICPHREG procedure "polishes" the hazard estimates by fixing these parameters at the lower bound value and refitting the model.

The lower bound values are set 0 if the baseline parameters are on the original scale (HAZSCALE=HAZARD). The values are set to –10.0 if they are on the log scale (HAZSCALE=LOGHAZ).

This option does not apply to the cubic spline model because its baseline parameters are unbounded.


specifies a variable in the input data set to be used as an offset variable. This variable cannot be a CLASS variable, the response variable, or any of the explanatory variables.


specifies a transformation to be applied to the baseline parameters for fitting the piecewise constant model. You can choose either of the following two options:


uses the log transformed baseline parameters.


does not transform the baseline parameters. A lower bound of 0 is used for fitting the models.

This option does not apply to the cubic spline model.