Survival estimate plots

Kaplan-Meier Survival Estimates Plot 204 SAS Tools for Creating Clinical Trial Graphs 205... 

Creating a Kaplan-Meier Survival Estimates Plot... 

The following is an example of a Kaplan-Meier survival estimates plot. In this plot, we are comparing the time to death for three different treatment regimens. The Kaplan-Meier survival estimate is on the Y axis, and time is represented on the X axis. Each step in the graph lines represents an event. 

On occasion it is necessary to produce failure estimate plots instead of survival estimates plots. Fortunately, this requires only a simple modification to the preceding Kaplan-Meier survival estimates program. The only changes necessary to this program to get a failure plot are to alter the title and axis labels, and to change the survival variable reference to failure because the failure variable is also present in the ProductLimitEstimates data set. The resulting failure estimate plot looks like the following ... 

Creating a Survival Estimates Plot Directly from PROC LIFETEST... 

The Kaplan-Meier survival estimates plots are instantiated by specifying PLOTS = (S) in the PROC LIFETEST statement. To show just the line itself, CENSOREDSYMBOL = NONE is specified to hide the censored observations in the plot. EVENTSYMBOL = NONE is specified here to hide the event points, although this is the default setting for... 

If you can accept the axis and legend definitions that are provided by the survival estimates plots from PROC LIFETEST, then this is a faster way to produce your plots than the PROG GPLOT method previously shown. It is only when you need precise control of your plot appearance that PROG GPLOT is necessary. 

Comparison of Kaplan-Meier survival estimates is often called for in clinical trial analysis. With survival analysis, you are trying to determine which treatment group displays a better time-to-event distribution than another. Part of this analysis is the production of Kaplan-Meier estimates plots that show the probability of a given event over time for each treatment group. In the following example you see that New Drug displays better survival estimates over time than either Old Drug or Placebo. ... 

PREPARE THE SURVIVAL ESTIMATE DATA FOR PLOTTING. data survest set survest by trt ... 

CREATE KM PLOT. SURVIVAL ESTIMATE IS ON THE Y AXIS, MONTHS ARE ON THE X AXIS, AND THE VALUES ARE PLOTTED BY TREATMENT. proc gplot... 

The Kaplan-Meier estimates produce a step function for each group and are plotted over the lifetime of the animals. Planned, accidentally killed, and lost animals are censored. Moribund deaths are considered to be treatment related. A graphical representation of Kaplan-Meier estimates provide excellent interpretation of survival adjusted data except in the cases where the curves cross between two or more groups. When the curves cross and change direction, no meaningful interpretation of the data can be made by any statistical method because proportional odds characteristic is totally lost over time. This would be a rare case where treatment initially produces more tumor or death and then, due to repair or other mechanisms, becomes beneficial. 

Kaplan and Meier (1958) introduced a methodology for estimating, from censored survival data, the probability of being event-free as a function of time. If the event is death then we are estimating the probability of surviving and the resultant plots of the estimated probability of surviving as a function of time are called either Kaplan-Meier (KM) curves or survival curves. 

Figure 10.5. Mass-age distribution of carbonate rocks and other sedimentary rock types plotted as survival rate (S) versus age. Total rock mass data from Gregor (1985) and estimates of carbonate rock mass from Table 10.1.

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