57 lines
2.0 KiB
R
57 lines
2.0 KiB
R
library(survival)
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#
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# Check that the survival curves from a Cox model with beta=0
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# match ordinary survival
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#
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# Aalen
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surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2)
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fit1 <- coxph(Surv(time, status) ~ age + strata(sex), data=lung, iter=0,
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ties='breslow')
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fit1$var <- 0*fit1$var #sneaky, causes the extra term in the Cox variance
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# calculation to be zero
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surv2 <- survfit(fit1, stype=2)
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surv3 <- survfit(fit1)
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arglist <- c('n', 'time', 'n.risk','n.event', 'n.censor', 'surv', 'strata',
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'std.err', 'upper', 'lower')
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all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
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all.equal(unclass(surv1)[arglist], unclass(surv3)[arglist])
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# Efron method
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surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2, ctype=2)
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surv2 <- survfit(fit1, ctype=2)
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all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
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# Kaplan-Meier
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surv1 <- survfit(Surv(time,status) ~ sex, data=lung)
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surv2 <- survfit(fit1, stype=1)
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all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
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# Now add some random weights
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rwt <- runif(nrow(lung), .5, 3)
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surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2, weights=rwt,
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robust=FALSE)
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fit1 <- coxph(Surv(time, status) ~ age + strata(sex), data=lung, iter=0,
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ties='breslow', weights=rwt, robust=FALSE)
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fit1$var <- 0*fit1$var #sneaky
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surv2 <- survfit(fit1, stype=2, ctype=1)
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surv3 <- survfit(fit1)
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all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
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all.equal(unclass(surv1)[arglist], unclass(surv3)[arglist])
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# Efron method
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surv1 <- survfit(Surv(time,status) ~ sex, data=lung, stype=2, ctype=2,
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weights=rwt, robust=FALSE)
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surv2 <- survfit(fit1, ctype=2, robust=FALSE)
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all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
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# Kaplan-Meier
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surv1 <- survfit(Surv(time,status) ~ sex, data=lung, weights=rwt, robust=FALSE)
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surv2 <- survfit(fit1, stype=1, robust=FALSE)
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all.equal(unclass(surv1)[arglist], unclass(surv2)[arglist])
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