Interpreting results: Kaplan-Meier curves

Kaplan-Meier survival fractions

Prism calculates survival fractions using the product limit (Kaplan-Meier) method. For each X value (time), Prism shows the fraction still alive (or the fraction already dead, if you chose to begin the curve at 0.0 rather than 1.0). This table contains the numbers used to graph survival vs. time.

Prism also reports the uncertainty of the fractional survival as a standard error or 95% confidence intervals. Standard errors are calculated by the method of Greenwood. The 95% confidence intervals are computed as 1.96 times the standard error in each direction. In some cases the confidence interval calculated this way would start below 0.0 or end above 1.0 (or 100%). In these cases, the error bars are clipped to avoid impossible values.

The calculations take into account censored observations. Subjects whose data are censored--either because they left the study, or because the study ended -- can't contribute any information beyond the time of censoring. This makes the computation of survival percentage somewhat tricky. While it seems intuitive that the curve ought to end at a survival fraction computed as the total number of subjects who died divided by the total number of subjects, this is only correct if there are no censored data. If some subjects were censored, then subjects were not all followed for the same duration, so computation of the survival fraction is not straightforward (and what the Kaplan-Meier method is for).

Number of subjects at risk at various times

Prism tabulates the number of patients still at risk at each time. The number of subjects still at risk decreases each time a subject dies or is censored.

Prism does not graph this table automatically. If you want to create a graph of number of subjects at risk over time, follow these steps: