> > survival ID time group status SurvObj 1 1 30 2 2 30 2 2 22 2 2 22 3 3 25 2 2 25 4 4 43 2 1 43+ 5 5 25 2 2 25 6 6 22 2 2 22 7 7 15 2 2 15 8 8 29 2 2 29 9 9 22 1 2 22 10 10 43 1 1 43+ 11 11 16 1 2 16 12 12 30 1 2 30 13 13 32 1 2 32 14 14 43 1 1 43+ 15 15 43 1 1 43+ 16 16 43 1 2 43 > # In group, 1=group of eight, 2= pair > # In status, 1=alive, 2=dead > survival$SurvObj <- with(survival, Surv(time, status == 2)) > head(survival) ID time group status SurvObj 1 1 30 2 2 30 2 2 22 2 2 22 3 3 25 2 2 25 4 4 43 2 1 43+ 5 5 25 2 2 25 6 6 22 2 2 22 > survival ID time group status SurvObj 1 1 30 2 2 30 2 2 22 2 2 22 3 3 25 2 2 25 4 4 43 2 1 43+ 5 5 25 2 2 25 6 6 22 2 2 22 7 7 15 2 2 15 8 8 29 2 2 29 9 9 22 1 2 22 10 10 43 1 1 43+ 11 11 16 1 2 16 12 12 30 1 2 30 13 13 32 1 2 32 14 14 43 1 1 43+ 15 15 43 1 1 43+ 16 16 43 1 2 43 > km.as.one <- survfit(SurvObj ~ 1, data = survival, conf.type = "log-log") > km.by.status <- survfit(SurvObj ~ status, data = survival, conf.type = "log-log") > km.as.one Call: survfit(formula = SurvObj ~ 1, data = survival, conf.type = "log-log") records n.max n.start events median 0.95LCL 0.95UCL 16.0 16.0 16.0 12.0 29.5 22.0 43.0 > summary(km.as.one) Call: survfit(formula = SurvObj ~ 1, data = survival, conf.type = "log-log") time n.risk n.event survival std.err lower 95% CI upper 95% CI 15 16 1 0.938 0.0605 0.6323 0.991 16 15 1 0.875 0.0827 0.5860 0.967 22 14 3 0.688 0.1159 0.4046 0.856 25 11 2 0.562 0.1240 0.2954 0.762 29 9 1 0.500 0.1250 0.2452 0.710 30 8 2 0.375 0.1210 0.1542 0.598 32 6 1 0.312 0.1159 0.1139 0.536 43 5 1 0.250 0.1083 0.0775 0.472 > plot(km.as.one) > km.by.group <- survfit(SurvObj ~ group, data = survival, conf.type = "log-log") > plot(km.by.group) > survdiff(SurvObj ~ group, data = survival, rho = 0) Call: survdiff(formula = SurvObj ~ group, data = survival, rho = 0) N Observed Expected (O-E)^2/E (O-E)^2/V group=1 8 5 7.42 0.792 2.43 group=2 8 7 4.58 1.284 2.43 Chisq= 2.4 on 1 degrees of freedom, p= 0.119 >