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To illustrate the use of PROC HAZARD and PROC HAZPRED a number of limited data sets, of a variety of types, are included to illustrate both the procedures and to provide a new user with practice. When you unbundle the hazard package, the examples will be stored in directory !HAZARD/examples, where !HAZARD is an environmental variable that points to the location of the hazard program on your system. Define the environment variable !HZEXAMPLES to point to !HAZARD/examples.
The first example is a short (n=310) data set with a few variables of the many studied is included as '!HZEXAMPLES/data/avc'. (The lesion is a form of congenital heart disease known as atrioventricular septal defects, or atrioventricular (AV) canal. The entire spectrum of lesions is included from partial through intermediate to complete forms, in that with this many cases, the data looked indeed like a spectrum, not 3 isolated forms.) The followup is such that at present, I find only two hazard phases: the early phase after the operation, and a constant hazard phase. As time goes on, undoubtedly the hazard will again rise, if for no other reason old age. Duration of followup time, as well as the actual distribution of events, will dictate how many phases of hazard are found (NOTE: we use "phases" as a more understandable term to clinicians than "mixture distribution components").
TheAVC example dataset is stored in a file named !HZEXAMPLES/examples/data/avc.
Normally this would be a SAS data set, probably built from the raw data. For illustration, a few variables from a data set for repair of atrioventricular septal defects will be read from a "flat file."
For a complete change of pace, another data set is included that has within it a repeated event (thromboembolism) and an evaluation of the seriousness of the event. In hz.te123 we look at two ways of analyzing such data: straight repeated event methodology using longitudinal segmentation of the observation as described in the documentation and left censoring, and a modulated renewal process. When the latter is valid, it makes for a much more interpretable analysis, in my opinion. In hz.tm123, the event is analyzed as a weighted outcome variable, using the WEIGHT statement.
The OMC example dataset is stored in a file named $HZEXAMPLES/examples/data/omc.
The CABGKUL example dataset is stored in a file named $HZEXAMPLES/examples/data/cabgkul.
Life Table for death after repair of atrioventricular septal defect
Determine Hazard Function for Death after repair of atrioventricular septal defects.
This job concentrates on determining the shaping parameter estimates for the overall (underlying) distribution of event. It is the major exercise that is different from Cox. It can be time-consuming at first, and has all the frustrations of nonlinear estimation. For me, after about 3000 of these exercises, the time varies from about 5 minutes to an hour. I use the non-parametric cumulative hazard function to provide all the clues. I start with simple models and work up (because it is easy to get into an overdetermined state). The final models are rarely complex. For example, an n=6000 coronary data set with 20 years followup will have all three phases present, with the hardest to fit late phase probably being a simple Weibul (late hazard=MUL*t**power), a constant hazard phase, and an early phase with M or NU likely simplifying to a constant (1 or 0). You need not bother with DELTA. But do note that sometimes the program will terminate abnormally, but with M or NU driven to near zero (E3 or E4 large negative numbers). THIS IS A CLUE that you want to set NU or M to zero (M=0 FIXM, for example). So when you get abnormal terminations, be sure that it is for something other than the computer trying to get close to minus infinity. There are 3 different optimization algorithms available, and we haven't found that we can do without all of them. In some problems, STEEPEST is really needed, in others QUASI works fine (sometimes in conjunction with STEEPEST), as does the Newton method. Sometimes the early phase will have some huge exponents--but there will be one of its 3 branches that will tame these exponents. Read the documentation--I know it is wordy. Don't become frustrated, however. We have had a number of users with initial start-up problems who have simply shipped us a data set of events and intervals and a description of their problems, and we have been able to help them get a good start. This effort is not terribly time- consuming for us, so do not hesitate in asking assistance.
Survival after primary isolated CABG. Hazard function for death.
Hazard function for repeated thromboembolic events.
Hazard function for permanent morbidity from thromboembolic events.
Descriptive analysis of death after repair. Transformations of scale investigation.
Multivariable analysis of death after repair
This job is an example of a multivariable analysis. As you see, the shaping parameters should be FIXED during the process of obtaining the risk factors. The actual process of doing this is no more difficult than a logistic or Cox modeling effort. We have tried, in fact, to make it a bit simpler by allowing you to a) exclude variables with an /E option but still look at their q-statistics for entry (equivalent to one Newton step), b) select variables with associated /S, and c) include variables with the /E option. These are all directly associated with the variables, so you do not have to move anything around--in particular you can keep your variables organized in their medically meaningful, and usually highly correlated, groups. A stepwise procedure is provided, and is useful for going a few steps (I rarely allow it to go more than 3-5 steps). But we don't recommend automatic model building in absence of knowledge of the quality of each variable and the covariance structure. The only intellectual hurdle you must overcome is looking simultaneously at multiple hazard phases. However, this "buys" relaxation of proportional hazards assumptions, and takes into account (especially in surgical series) the well established observation that some risk factors are strong early after operation, and much less so later. The job also shows that as a final run, we would then suggest unfixing the shaping parameters, specifying all the estimates and making a final run. In theory, the shaping parameter values must change--we have been surprised by how little they change (that is, the average curve is not the same as setting all risk factors to zero). You may wish also to reexplore the early phase shaping parameters in the rare event that THALF gets much larger in this final step.
These are examples of exploring the strengths of risk factors either at a slice across time (hm.death.hm1) or across time (hm.death.hm1). The advantage of an all parametric model is that all you have to do is solve an equation!
Multivariable analysis of death after repair of atrioventricular canals.
Exploration of strength of risk factors. A major strength of completely parametric models is that once parameter estimates are available, the resulting equation can be solved for any given set of risk factors. This permits, for example, solving the equation for the time-related survival of an individual patient by "plugging in" that patient's specific risk factors (patient-specific prediction).
In this example, we exploit the parametric model by exploring the shape of risk factors. Here, for a given set of risk factors, we compare survival in two otherwise similar patients, except that one has an additional major cardiac anomaly.
- Multivariable analysis of death after repair
- Exploration of strength of risk factors.
- Display strength of date of repair in partial and complete forms of AV Canal
Multivariable analysis of death after repair
Another strength of a completely parametric survival analysis is that the investigator can test the "goodness" of the model. Specifically, the theory of mixture distributions would indicate that if a survival curve (or death density function) is generated for each observation, the mean of these should be the overall survival curve. And for any subset of the data, such a subset mean should well fit the empiric survival curve (unless a risk factor has not been taken into account).
The theory of conservation of events also suggests that we can sum the cumulative hazard for each patient and predict the number of expected deaths, comparing this with the actual number. One has to be a bit careful here, since the cumulative hazard has a limitless upper bound. One could make the case for limiting any cumulative hazard estimate greater than 1.0 to that number.
As a type of internal validation, although the variable is in the model, we'll predict partial and complete AV Canal from the analysis, overlaying it with the nonparametric life table estimates to check out the model. Recall that we were concerned that the log-likelihood was better when COM_IV was present in the constant hazard phase, but we could not obtain reliable estimates for that factor. This will help us see how badly we miss the mark by ignoring this factor in that phase.
In actual practice, what I do is to have a "setup" job that generates the curves for each patient in the data set and the cumulative hazard values. Once stored (often temporarily, for the data set size can be huge), I can then stratify the data set in every which way to check myself out using a separate job or set of jobs.
For sheer practice of fitting a variety of hazard shapes with up to three phases, you may now wish to explore the coronary artery bypass grafting data set. It contains the event death, return of angina, and reintervention. To get you started, hz.deadp.KUL is an annotated setup for the event death. You will want to start off with some actuarial estimates, as you did for the data set above, and work your way through all these events.
For practice at both fitting interesting hazard shapes, controversy as to what events should be "bundled" as a single event, and then exercising of the procedure's variable selection possibilities, a limited valve replacement data set ($HAZARD/examples/data/valves) is provided, along with a sample multivariable setup program hm.deadp.VALVES.
Since an important aspect of the model is exercising the resulting equations, we have included a number of plots (hp. jobs) as well as a number of validation files (the hs jobs). These are internally commented.
Another use of predictions is for comparing alternative treatments from analyses of different data sets. One example of this is given in hp.death.COMPARISON, an example of comparison of PTCA, CABG and medical treatment.