Kaplan-Meier

Creating Kaplan-Meier Survival Estimates Tables... 

Table 5.7 Kaplan-Meier Survival Estimates for Death Over Time ...

You can see that the New Drug displays better survival probabilities over time than Old Drug or Placebo. You can easily convert this table to a table of Kaplan-Meier failure estimates by replacing survprob = survival with survprob = failure in Program 5.7. 

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

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. ... 

Kaplan-Meier plot PROC LIFETEST or PROC GPLOT... 

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. 

Here is the SAS program that creates this Kaplan-Meier estimates plot. 

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 ... 

CREATE KAPLAN-MEIER PLOT WITH PROC LIFETEST. proc lifetest data = death plots = (s) censoredsymbol = none eventsymbol = none ... 

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... 

Creating a Kaplan-Meier Estimates Plot Using PROC GPLOT 233... 

These methods are essential when there is any significant degree of mortality in a bioassay. They seek to adjust for the differences in periods of risk individual animals undergo. Life table techniques can be used for those data where there are observable or palpable tumors. Specifically, one should use Kaplan-Meier product limit estimates from censored data graphically, Cox-Tarone binary regression (log-rank test), and Gehan-Breslow modification of Kruskal-Wallis tests (Thomas et al., 1977 Portier and Bailer, 1989) on censored data. 

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. 

Life tables can be constructed to provide estimates of the event time distributions. Estimates commonly used are known as the Kaplan-Meier estimates. 

Kaplan-Meier overall survival at 5 years was 52% (95% Cl 43-61%) for patients of HLA-matched group and 51% (95% Cl 33-69%) in HLA-mismatched group.The following variables were associated with improved patient survival in univariate analysis patient age <20 years (p=0.006), nonmalignant disease vs all others (p=0.01), AML (p=0.04), standard risk disease (p=0.0002), CMV seronegative patients (p=0.002). In multivariate analysis relative risks were determined as per Table 5. 

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