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Complex Samples Cox Regression
Define Time-Dependent Predictor
Figure 12-4
Cox Regression Define Time-Dependent Predictor dialog box
The Define Time-Dependent Predictor dialog box allows you to create a predictor that is
dependent upon the built-in time variable, T_. You can use this variable to define time-dependent
covariates in two general ways:
If you want to estimate an extended Cox regression model that allows nonproportional
hazards, you can do so by defining your time-dependent predictor as a function of the time
variable T_ and the covariate in question. A common example would be the simple product of
the time variable and the predictor, but more complex functions can be specified as well.
Some variables may have different values at different time periods but aren’t systematically
relatedtotime.Insuchcases,youneedtodefine a segmented time-dependent predictor,
which can be done using logical expressions. Logical expressions take the value 1 if true
and 0 if false. Using a series of logical expressions, you can create your time-dependent
predictor from a set of measurements. For example, if you have blood pressure measured
once a week for the four weeks of your study (identified as BP1 to BP4), you can define your
time-dependent predictor as (T_ <1)*BP1 +(T_ >= 1 & T_ <2)*BP2 +(T_ >= 2 & T_ <3)
* BP3 +(T_ >= 3 & T_ <4)*BP4. Notice that exactly one of the terms in parentheses will
be equal to 1 for any given case and the rest will all equal 0. In other words, this function
meansthatiftimeislessthanoneweek,useBP1; if it is more than one week but less than
two weeks, use BP2; and so on.