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Included in '!HAZARD/macros' are also a number of MACROS that we have found useful.
This macro implements for the hazard procedure bootstrap so-called "bagging" for variable selection. In its use, you will find that one of the most important features of the hazard variable selection is the RESTRICT statement. This allows you to identify closely linked variables (such as transformations of age, all variables related to patient size, and so forth), such that only one of each kind of variable is selected. Then, a count, either manually or by machine, can be made of the unique occurrences of variables.
This is a Chi-square goodness-of-fit macro for comparing expected and observed events. It is called by hazard simulation jobs that we have designated as hs. in the examples.
This macro sorts patients according to survival at a given point in time specified by the TIME= variable, and then examines expected and observed events within groups specified in number by the variable GROUPS=. This routine is useful in examining goodness-of-fit of multivariable models.
This macro is useful for determining the goodness-of-fit of your hazard function to the distribution of times to the event. We use the Kaplan-Meier method as an independent verification tool. The macro uses various transformations of scale to examine early and late time frames to indicate the correspondence between the parametric and nonparametric estimates. In addition, it compares observed and predicted numbers of deaths and the departure of these from one another. This is perhaps one of the best clues to lack of fit. It also indicates how many events are associated with each hazard phase so that you can avoid overdetermined models for each hazard phase.
This is a Kaplan-Meier life table analysis macro. Its only difference from most packaged Kaplan-Meier programs is in its calculation of confidence limits. The confidence limits used in this program are more exact than using the Greenwood formula.
This macro is one of two that is useful in calibrating continuous or ordinal variables to the event. It is useful for logistic regression as well as time-related analyses. It groups the patients in roughly equal numbers, examines the number of events, and displays these on the scale of risk that is appropriate for your analysis. Ideally, if there is an association between the variable and risk, it should align nicely and linearly on the scale of risk. We usually examine a number of different transformations of scale of the variable to see which aligns best.
This macro is similar to logit.sas except that, instead of dividing the groups into equal numbers, it divides the groups according to a specified grouping variable. Otherwise, the information is similar.
This macro calculates nonparametric estimates for weighted repeated events according to the algorithm of Wayne Nelson. Unlike the Kaplan-Meier estimator, the Nelson estimator is defined in the cumulative hazard domain. The latter is particularly more suited to repeated events and specifically to weighted events (Nelson calls it the cost function).
This macro is a companion to kaplan.sas, with identical outputs and naming of variables. However, it calculates the life table by the method of Wayne Nelson, rather than by the method of Kaplan and Meier. In this method, an event that plunges the life table calculated by Kaplan and Meier to zero is instead set to a value commensurate with the cumulative hazard function.
This is a big macro for making "pretty plots". Because of changes made in some of the SAS® graph modules between version 6 and version 8, we do not recommend as yet its use for version 8. We are working on redoing those sections of the macro that have been changed in version 8.
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