57 lines
1.6 KiB
R
57 lines
1.6 KiB
R
options(na.action=na.exclude) # preserve missings
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options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
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library(survival)
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#
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# Test some more features of surv.diff
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#
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# First, what happens when one group is a dummy
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#
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#
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# The AML data, with a third group of early censorings "tacked on"
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#
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aml3 <- list(time= c( 9, 13, 13, 18, 23, 28, 31, 34, 45, 48, 161,
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5, 5, 8, 8, 12, 16, 23, 27, 30, 33, 43, 45,
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1, 2, 2, 3, 3, 3, 4),
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status= c( 1,1,0,1,1,0,1,1,0,1,0, 1,1,1,1,1,0,1,1,1,1,1,1,
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0,0,0,0,0,0,0),
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x = as.factor(c(rep("Maintained", 11),
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rep("Nonmaintained", 12), rep("Dummy",7) )))
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aml3 <- data.frame(aml3)
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# These should give the same result (chisq, df), but the second has an
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# extra group
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survdiff(Surv(time, status) ~x, aml)
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survdiff(Surv(time, status) ~x, aml3)
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#
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# Now a test of the stratified log-rank
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# There are no tied times within institution, so the coxph program
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# can be used to give a complete test
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#
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fit <- survdiff(Surv(time, status) ~ pat.karno + strata(inst), lung)
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cfit <- coxph(Surv(time, status) ~ factor(pat.karno) + strata(inst),
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lung, iter=0)
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tdata <- na.omit(lung[,c('time', 'status', 'pat.karno', 'inst')])
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temp1 <- tapply(tdata$status-1, list(tdata$pat.karno, tdata$inst), sum)
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temp1 <- ifelse(is.na(temp1), 0, temp1)
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temp2 <- tapply(cfit$resid, list(tdata$pat.karno, tdata$inst), sum)
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temp2 <- ifelse(is.na(temp2), 0, temp2)
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temp2 <- temp1 - temp2
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#Now temp1=observed, temp2=expected
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all.equal(c(temp1), c(fit$obs))
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all.equal(c(temp2), c(fit$exp))
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all.equal(fit$var[-1,-1], solve(cfit$var))
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rm(tdata, temp1, temp2)
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