By Paul D Allison

Biomedical and social technological know-how researchers who are looking to examine survival information with SAS will locate simply what they want with this easy-to-read and accomplished consultant. Written for the reader with a modest statistical heritage and minimum wisdom of SAS software program, this ebook teaches many features of information enter and manipulation. a number of examples of SAS code and output make this an eminently sensible source, making sure that even the uninitiated turns into a worldly person of survival research. the most subject matters offered contain censoring, survival curves, Kaplan-Meier estimation, sped up failure time versions, Cox regression versions, and discrete-time research. additionally incorporated are themes now not frequently coated, reminiscent of time-dependent covariates, competing dangers, and repeated occasions.

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This fact suggests that the hazard is not constant, but rather declines with time. If the plot had curved upward rather than downward, it would suggest that the hazard was increasing with time. Of course, since the sample size is quite small, caution is advisable in drawing any conclusions. A formal test, such as the one described in the next chapter, might not show a significant decrease in the hazard. 14 Log-Survival Plot for Myelomatosis Data CHAPTER 3 Estimating and Comparing Survival Curves with PROC LIFETEST 57 The LLS keyword produces a plot of log[-log S(t)] versus log t.

The third column, labeled Failure, is just 1 minus the KM estimate, which is the estimated probability of a death prior to the specified time. The fourth column, labeled Survival Standard Error, is an estimate of the standard error of the KM estimate, obtained by the well-known Greenwood's formula (Collett 1994, p. 23). You can use this estimated standard error to construct confidence intervals around the KM estimates. The fifth column, labeled Number Failed, is just the cumulative number of cases that experienced events prior to and including each point in time.

In principle, the hazard for the occurrence of a particular kind of event can be a function of all of these times or any subset of them. Nevertheless, the continuous-time methods considered in this book require a choice of a single time origin. (The discrete time methods discussed in Chapter 7 are more flexible in that regard). Although you can sometimes include time measurements based on other origins as covariates, that strategy usually restricts the choice of models and may require more demanding computation.

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