I am running proc lifetest on a dataset that has no censored observation, only events.
At T1 (time) = 1, Number at Risk = 33 so the survival probability should be 33/46 = 0.717.=? Why does SAS give me 0.695?
Hi @CHELS,
The numbers of subjects at risk (denoted by Yi in section Breslow, Fleming-Harrington, and Kaplan-Meier Methods of the PROC LIFETEST documentation) are counted "just prior to" the respective time points, whereas the (estimated) "Survival Probability" (here: Kaplan-Meier survival estimate) takes the events at the respective time points already into account. So, at T1=1 the 13 events prior to T1=1 have left 33 of the 46 subjects in stratum 1. Then the survival estimate is updated with the 14th event, occurring at T1=1: The previous estimate (33/46=0.717...) is multiplied by (1-1/33) resulting in 32/46=0.695...
Show as a minimum the code you are using.
Did you look closely at row 12 and the probability. Looks awful close you your expected. Perhaps you are missing a consideration of which row you need to examine.
Did you specify Method = LT? Or a different method? How did you choose to handle ties?
@CHELS wrote:
I am running proc lifetest on a dataset that has no censored observation, only events.
At T1 (time) = 1, Number at Risk = 33 so the survival probability should be 33/46 = 0.717.=? Why does SAS give me 0.695?
Hi @CHELS,
The numbers of subjects at risk (denoted by Yi in section Breslow, Fleming-Harrington, and Kaplan-Meier Methods of the PROC LIFETEST documentation) are counted "just prior to" the respective time points, whereas the (estimated) "Survival Probability" (here: Kaplan-Meier survival estimate) takes the events at the respective time points already into account. So, at T1=1 the 13 events prior to T1=1 have left 33 of the 46 subjects in stratum 1. Then the survival estimate is updated with the 14th event, occurring at T1=1: The previous estimate (33/46=0.717...) is multiplied by (1-1/33) resulting in 32/46=0.695...
Save $250 on SAS Innovate and get a free advance copy of the new SAS For Dummies book! Use the code "SASforDummies" to register. Don't miss out, May 6-9, in Orlando, Florida.
ANOVA, or Analysis Of Variance, is used to compare the averages or means of two or more populations to better understand how they differ. Watch this tutorial for more.
Find more tutorials on the SAS Users YouTube channel.