655 lines
16 KiB
Plaintext
655 lines
16 KiB
Plaintext
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R Under development (unstable) (2024-02-07 r85873) -- "Unsuffered Consequences"
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Copyright (C) 2024 The R Foundation for Statistical Computing
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Platform: x86_64-pc-linux-gnu
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R is free software and comes with ABSOLUTELY NO WARRANTY.
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You are welcome to redistribute it under certain conditions.
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Type 'license()' or 'licence()' for distribution details.
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R is a collaborative project with many contributors.
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Type 'contributors()' for more information and
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'citation()' on how to cite R or R packages in publications.
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Type 'demo()' for some demos, 'help()' for on-line help, or
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'help.start()' for an HTML browser interface to help.
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Type 'q()' to quit R.
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> 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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> mdy.date <- function(m, d, y) {
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+ y <- ifelse(y<100, y+1900, y)
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+ as.Date(paste(m,d,y, sep='/'), "%m/%d/%Y")
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+ }
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>
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> #
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> # Simple case: a single male subject, born 6/6/36 and entered on study 6/6/55.
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> #
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>
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> temp1 <- mdy.date(6,6,36)
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> temp2 <- mdy.date(6,6,55)# Now compare the results from person-years
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> #
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> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:31 * 365.24))),
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+ labels=c('0-18', paste(18:30, 19:31, sep='-')))
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> temp.yr <- tcut(temp2, mdy.date(1,1,1954:1965), labels=1954:1964)
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> temp.time <- 3700 #total days of fu
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> py1 <- pyears(temp.time ~ temp.age + temp.yr, scale=1) #output in days
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>
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> # The subject should appear in 20 cells
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> # 6/6/55 - 12/31/55, 209 days, age 19-20, 1955
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> # 1/1/56 - 6/ 4/56, 156 days, age 19-20, 1956
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> # 6/5/56 - 12/31/56, 210 days, age 20-21, 1956 (a leap year, and his
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> # birthday computes one day earlier)
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> # 1/1/57 - 6/ 5/57, 156 days, age 20-21, 1957
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> # 6/6/57 - 12/31/57, 209 days, age 21-22, 1957
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> # and etc
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> # with 203 days "off table", ie, beyond the last cell of the table
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> #
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> # It is a nuisance, but tcut follows 'cut' in that we give the ENDS of
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> # the intervals, whereas the survival tables use the starts of intervals.
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> # Thus this breakdown does not match that in doexpect.s
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> #
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> xx <- matrix(0, nrow=14, ncol=11)
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> xx[cbind(3:11, 3:11)] <- 156
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> xx[cbind(3:12, 2:11)] <- c(209, 210, rep(c(209, 209, 209, 210),2))
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> dimnames(xx) <- list(temp.age= c('0-18', paste(18:30, 19:31, sep='-')),
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+ temp.yr = 1954:1964)
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> all.equal(xx, py1$pyears)
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[1] TRUE
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> all.equal(203, py1$offtable)
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[1] TRUE
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> all.equal(1*(xx>0), py1$n)
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[1] TRUE
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>
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> #
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> # Now with expecteds
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> #
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> py2 <- pyears(temp.time ~ temp.age + temp.yr,
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+ rmap=list(age=temp2-temp1, year=temp2, sex=1),
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+ scale=1, ratetable=survexp.us ) #output in days
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> all.equal(xx, py2$pyears)
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[1] TRUE
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> all.equal(203, py2$offtable)
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[1] TRUE
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> all.equal(1*(xx>0), py2$n)
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[1] TRUE
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>
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>
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> py3 <- pyears(temp.time ~ temp.age + temp.yr,
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+ rmap=list(age=temp2-temp1, year=temp2, sex=1),
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+ scale=1, ratetable=survexp.us , expect='pyears')
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> all.equal(py2$n, py3$n)
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[1] TRUE
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> all.equal(py2$pyear, py3$pyear)
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[1] TRUE
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> all.equal(py3$n, 1*(py3$expect>0))
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[1] TRUE
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>
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> # Now, compute the py3 result "by hand". Since there is only one person
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> # it can be derived from py2.
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> #
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> xx1 <- py2$expect[py2$n>0] # the hazard over each interval
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> cumhaz <- cumsum(c(0, xx1[-length(xx1)])) # the cumulative hazard
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> xx2 <- py3$expect[py3$n>0] # the expected number of person days
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> xx3 <- py3$pyears[py3$n>0] # the potential number of person days
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>
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> # This is the integral of the curve "exp(-haz *t)" over the interval
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> integral <- xx3 * exp(-cumhaz)* (1- exp(-xx1))/ xx1
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> # They might not be exactly equal, since the C code tracks changes in the
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> # rate tables that occur -within- an interval. So try for 6 digits
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> all.equal(round(integral,3), round(xx2,3))
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[1] TRUE
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>
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> # Cut off the bottom of the table, instead of the side
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> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:27 * 365.24))),
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+ labels=c('0-18', paste(18:26, 19:27, sep='-')))
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>
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> py4 <- eval(py3$call)
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> all.equal(py4$pyear, py3$pyear[1:10,])
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[1] TRUE
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> all.equal(py4$expect, py3$expect[1:10,])
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[1] TRUE
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>
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>
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> rm(temp.age, integral, xx1, xx2, xx3, cumhaz, py1, py2, py3, py4)
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> rm(temp1, temp2, temp.yr, temp.time, xx)
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>
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>
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>
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>
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> #
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> # Simple case: a single male subject, born 6/6/36 and entered on study 6/6/55.
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> #
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>
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> temp1 <- mdy.date(6,6,36)
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> temp2 <- mdy.date(6,6,55)# Now compare the results from person-years
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> #
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> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:31 * 365.24))),
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+ labels=c('0-18', paste(18:30, 19:31, sep='-')))
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> temp.yr <- tcut(temp2, mdy.date(1,1,1954:1965), labels=1954:1964)
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> temp.time <- 3700 #total days of fu
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> py1 <- pyears(temp.time ~ temp.age + temp.yr, scale=1) #output in days
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>
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> # The subject should appear in 20 cells
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> # 6/6/55 - 12/31/55, 209 days, age 19-20, 1955
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> # 1/1/56 - 6/ 4/56, 156 days, age 19-20, 1956
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> # 6/5/56 - 12/31/56, 210 days, age 20-21, 1956 (a leap year, and his
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> # birthday computes one day earlier)
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> # 1/1/57 - 6/ 5/57, 156 days, age 20-21, 1957
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> # 6/6/57 - 12/31/57, 209 days, age 21-22, 1957
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> # and etc
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> # with 203 days "off table", ie, beyond the last cell of the table
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> #
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> # It is a nuisance, but tcut follows 'cut' in that we give the ENDS of
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> # the intervals, whereas the survival tables use the starts of intervals.
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> #
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> xx <- matrix(0, nrow=14, ncol=11)
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> xx[cbind(3:11, 3:11)] <- 156
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> xx[cbind(3:12, 2:11)] <- c(209, 210, rep(c(209, 209, 209, 210),2))
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> dimnames(xx) <- list(temp.age= c('0-18', paste(18:30, 19:31, sep='-')),
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+ temp.yr = 1954:1964)
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> all.equal(xx, py1$pyears)
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[1] TRUE
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> all.equal(203, py1$offtable)
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[1] TRUE
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> all.equal(1*(xx>0), py1$n)
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[1] TRUE
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>
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> #
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> # Now with expecteds
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> #
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> py2 <- pyears(temp.time ~ temp.age + temp.yr,
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+ rmap= list(age=temp2-temp1, year=temp2, sex=1),
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+ scale=1, ratetable=survexp.us ) #output in days
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> all.equal(xx, py2$pyears)
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[1] TRUE
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> all.equal(203, py2$offtable)
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[1] TRUE
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> all.equal(1*(xx>0), py2$n)
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[1] TRUE
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>
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>
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> py3 <- pyears(temp.time ~ temp.age + temp.yr,
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+ rmap= list(age=temp2-temp1, year=temp2, sex=1),
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+ scale=1, ratetable=survexp.us , expect='pyears')
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> all.equal(py2$n, py3$n)
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[1] TRUE
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> all.equal(py2$pyear, py3$pyear)
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[1] TRUE
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> all.equal(py3$n, 1*(py3$expect>0))
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[1] TRUE
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>
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> # Now, compute the py3 result "by hand". Since there is only one person
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> # it can be derived from py2.
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> #
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> xx1 <- py2$expect[py2$n>0] # the hazard over each interval
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> cumhaz <- cumsum(c(0, xx1[-length(xx1)])) # the cumulative hazard
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> xx2 <- py3$expect[py3$n>0] # the expected number of person days
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> xx3 <- py3$pyears[py3$n>0] # the potential number of person days
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>
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> # This is the integral of the curve "exp(-haz *t)" over the interval
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> integral <- xx3 * exp(-cumhaz)* (1- exp(-xx1))/ xx1
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> # They might not be exactly equal, since the C code tracks changes in the
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> # rate tables that occur -within- an interval. So try for 6 digits
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> all.equal(round(integral,3), round(xx2,3))
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[1] TRUE
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>
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> # Cut off the bottom of the table, instead of the side
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> temp.age <- tcut(temp2-temp1, floor(c(-1, (18:27 * 365.24))),
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+ labels=c('0-18', paste(18:26, 19:27, sep='-')))
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>
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> py4 <- eval(py3$call)
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> all.equal(py4$pyear, py3$pyear[1:10,])
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[1] TRUE
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> all.equal(py4$expect, py3$expect[1:10,])
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[1] TRUE
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>
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>
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> rm(temp.age, integral, xx1, xx2, xx3, cumhaz, py1, py2, py3, py4)
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> rm(temp1, temp2, temp.yr, temp.time, xx)
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>
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>
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>
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>
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> #
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> # Create a "user defined" rate table, using the smoking data
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> #
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> temp <- scan("data.smoke")/100000
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Read 224 items
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> temp <- matrix(temp, ncol=8, byrow=T)
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> smoke.rate <- c(rep(temp[,1],6), rep(temp[,2],6), temp[,3:8])
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> attributes(smoke.rate) <- list(
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+ dim=c(7,2,2,6,3),
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+ dimnames=list(c("45-49","50-54","55-59","60-64","65-69","70-74","75-79"),
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+ c("1-20", "21+"),
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+ c("Male","Female"),
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+ c("<1", "1-2", "3-5", "6-10", "11-15", ">=16"),
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+ c("Never", "Current", "Former")),
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+ dimid=c("age", "amount", "sex", "duration", "status"),
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+ factor=c(0,1,1,0,1),
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+ cutpoints=list(c(45,50,55,60,65,70,75),NULL, NULL,
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+ c(0,1,3,6,11,16),NULL),
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+ class='ratetable'
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+ )
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> rm(temp)
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>
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> is.ratetable(smoke.rate)
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[1] TRUE
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> summary(smoke.rate)
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Rate table with 5 dimensions:
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age ranges from 45 to 75; with 7 categories
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amount has levels of: 1-20 21+
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sex has levels of: Male Female
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duration ranges from 0 to 16; with 6 categories
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status has levels of: Never Current Former
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> print(smoke.rate)
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Rate table with dimension(s): age amount sex duration status
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, , Male, <1, Never
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1-20 21+
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45-49 0.001860 0.001860
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50-54 0.002556 0.002556
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55-59 0.004489 0.004489
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60-64 0.007337 0.007337
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65-69 0.011194 0.011194
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70-74 0.020705 0.020705
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75-79 0.036753 0.036753
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, , Female, <1, Never
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1-20 21+
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45-49 0.001257 0.001257
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50-54 0.001773 0.001773
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55-59 0.002448 0.002448
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60-64 0.003977 0.003977
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65-69 0.006921 0.006921
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70-74 0.011600 0.011600
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75-79 0.020708 0.020708
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, , Male, 1-2, Never
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1-20 21+
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45-49 0.001860 0.001860
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50-54 0.002556 0.002556
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55-59 0.004489 0.004489
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60-64 0.007337 0.007337
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65-69 0.011194 0.011194
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70-74 0.020705 0.020705
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75-79 0.036753 0.036753
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, , Female, 1-2, Never
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1-20 21+
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45-49 0.001257 0.001257
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50-54 0.001773 0.001773
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55-59 0.002448 0.002448
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60-64 0.003977 0.003977
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65-69 0.006921 0.006921
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70-74 0.011600 0.011600
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75-79 0.020708 0.020708
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, , Male, 3-5, Never
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1-20 21+
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45-49 0.001860 0.001860
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50-54 0.002556 0.002556
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55-59 0.004489 0.004489
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60-64 0.007337 0.007337
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65-69 0.011194 0.011194
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70-74 0.020705 0.020705
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75-79 0.036753 0.036753
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, , Female, 3-5, Never
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1-20 21+
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45-49 0.001257 0.001257
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50-54 0.001773 0.001773
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55-59 0.002448 0.002448
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60-64 0.003977 0.003977
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65-69 0.006921 0.006921
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70-74 0.011600 0.011600
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75-79 0.020708 0.020708
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, , Male, 6-10, Never
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1-20 21+
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45-49 0.001860 0.001860
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50-54 0.002556 0.002556
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55-59 0.004489 0.004489
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60-64 0.007337 0.007337
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65-69 0.011194 0.011194
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70-74 0.020705 0.020705
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75-79 0.036753 0.036753
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, , Female, 6-10, Never
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1-20 21+
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45-49 0.001257 0.001257
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50-54 0.001773 0.001773
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55-59 0.002448 0.002448
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60-64 0.003977 0.003977
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65-69 0.006921 0.006921
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70-74 0.011600 0.011600
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75-79 0.020708 0.020708
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, , Male, 11-15, Never
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1-20 21+
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45-49 0.001860 0.001860
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50-54 0.002556 0.002556
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55-59 0.004489 0.004489
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60-64 0.007337 0.007337
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65-69 0.011194 0.011194
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70-74 0.020705 0.020705
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75-79 0.036753 0.036753
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, , Female, 11-15, Never
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1-20 21+
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45-49 0.001257 0.001257
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50-54 0.001773 0.001773
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55-59 0.002448 0.002448
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60-64 0.003977 0.003977
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65-69 0.006921 0.006921
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70-74 0.011600 0.011600
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75-79 0.020708 0.020708
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, , Male, >=16, Never
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1-20 21+
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45-49 0.001860 0.001860
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50-54 0.002556 0.002556
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55-59 0.004489 0.004489
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60-64 0.007337 0.007337
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65-69 0.011194 0.011194
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70-74 0.020705 0.020705
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75-79 0.036753 0.036753
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, , Female, >=16, Never
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1-20 21+
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45-49 0.001257 0.001257
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50-54 0.001773 0.001773
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55-59 0.002448 0.002448
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60-64 0.003977 0.003977
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65-69 0.006921 0.006921
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70-74 0.011600 0.011600
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75-79 0.020708 0.020708
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, , Male, <1, Current
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1-20 21+
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45-49 0.004392 0.006100
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50-54 0.007027 0.009156
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55-59 0.011324 0.013910
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60-64 0.019811 0.023934
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65-69 0.030030 0.034979
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70-74 0.046975 0.058613
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75-79 0.073406 0.062500
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, , Female, <1, Current
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1-20 21+
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45-49 0.002256 0.002779
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50-54 0.003538 0.005179
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55-59 0.005428 0.008235
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60-64 0.008580 0.013029
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65-69 0.014962 0.019349
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70-74 0.020848 0.028270
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75-79 0.033195 0.042731
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, , Male, 1-2, Current
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1-20 21+
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45-49 0.004392 0.006100
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50-54 0.007027 0.009156
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55-59 0.011324 0.013910
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60-64 0.019811 0.023934
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65-69 0.030030 0.034979
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70-74 0.046975 0.058613
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75-79 0.073406 0.062500
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, , Female, 1-2, Current
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1-20 21+
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45-49 0.002256 0.002779
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50-54 0.003538 0.005179
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55-59 0.005428 0.008235
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60-64 0.008580 0.013029
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65-69 0.014962 0.019349
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70-74 0.020848 0.028270
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75-79 0.033195 0.042731
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, , Male, 3-5, Current
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1-20 21+
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45-49 0.004392 0.006100
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50-54 0.007027 0.009156
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55-59 0.011324 0.013910
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60-64 0.019811 0.023934
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65-69 0.030030 0.034979
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70-74 0.046975 0.058613
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75-79 0.073406 0.062500
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, , Female, 3-5, Current
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1-20 21+
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|
45-49 0.002256 0.002779
|
|
50-54 0.003538 0.005179
|
|
55-59 0.005428 0.008235
|
|
60-64 0.008580 0.013029
|
|
65-69 0.014962 0.019349
|
|
70-74 0.020848 0.028270
|
|
75-79 0.033195 0.042731
|
|
|
|
, , Male, 6-10, Current
|
|
|
|
1-20 21+
|
|
45-49 0.004392 0.006100
|
|
50-54 0.007027 0.009156
|
|
55-59 0.011324 0.013910
|
|
60-64 0.019811 0.023934
|
|
65-69 0.030030 0.034979
|
|
70-74 0.046975 0.058613
|
|
75-79 0.073406 0.062500
|
|
|
|
, , Female, 6-10, Current
|
|
|
|
1-20 21+
|
|
45-49 0.002256 0.002779
|
|
50-54 0.003538 0.005179
|
|
55-59 0.005428 0.008235
|
|
60-64 0.008580 0.013029
|
|
65-69 0.014962 0.019349
|
|
70-74 0.020848 0.028270
|
|
75-79 0.033195 0.042731
|
|
|
|
, , Male, 11-15, Current
|
|
|
|
1-20 21+
|
|
45-49 0.004392 0.006100
|
|
50-54 0.007027 0.009156
|
|
55-59 0.011324 0.013910
|
|
60-64 0.019811 0.023934
|
|
65-69 0.030030 0.034979
|
|
70-74 0.046975 0.058613
|
|
75-79 0.073406 0.062500
|
|
|
|
, , Female, 11-15, Current
|
|
|
|
1-20 21+
|
|
45-49 0.002256 0.002779
|
|
50-54 0.003538 0.005179
|
|
55-59 0.005428 0.008235
|
|
60-64 0.008580 0.013029
|
|
65-69 0.014962 0.019349
|
|
70-74 0.020848 0.028270
|
|
75-79 0.033195 0.042731
|
|
|
|
, , Male, >=16, Current
|
|
|
|
1-20 21+
|
|
45-49 0.004392 0.006100
|
|
50-54 0.007027 0.009156
|
|
55-59 0.011324 0.013910
|
|
60-64 0.019811 0.023934
|
|
65-69 0.030030 0.034979
|
|
70-74 0.046975 0.058613
|
|
75-79 0.073406 0.062500
|
|
|
|
, , Female, >=16, Current
|
|
|
|
1-20 21+
|
|
45-49 0.002256 0.002779
|
|
50-54 0.003538 0.005179
|
|
55-59 0.005428 0.008235
|
|
60-64 0.008580 0.013029
|
|
65-69 0.014962 0.019349
|
|
70-74 0.020848 0.028270
|
|
75-79 0.033195 0.042731
|
|
|
|
, , Male, <1, Former
|
|
|
|
1-20 21+
|
|
45-49 0.002344 0.004975
|
|
50-54 0.005447 0.004828
|
|
55-59 0.009452 0.017571
|
|
60-64 0.011777 0.015784
|
|
65-69 0.022449 0.023018
|
|
70-74 0.042553 0.031746
|
|
75-79 0.058824 0.040000
|
|
|
|
, , Female, <1, Former
|
|
|
|
1-20 21+
|
|
45-49 0.000000 0.002667
|
|
50-54 0.001168 0.001387
|
|
55-59 0.002874 0.004736
|
|
60-64 0.010163 0.011148
|
|
65-69 0.011080 0.023196
|
|
70-74 0.006452 0.046358
|
|
75-79 0.000000 0.024096
|
|
|
|
, , Male, 1-2, Former
|
|
|
|
1-20 21+
|
|
45-49 0.003658 0.002517
|
|
50-54 0.004310 0.005007
|
|
55-59 0.007288 0.009535
|
|
60-64 0.015892 0.018472
|
|
65-69 0.033803 0.037766
|
|
70-74 0.050830 0.029740
|
|
75-79 0.065972 0.044248
|
|
|
|
, , Female, 1-2, Former
|
|
|
|
1-20 21+
|
|
45-49 0.004339 0.001027
|
|
50-54 0.000921 0.004668
|
|
55-59 0.002595 0.006020
|
|
60-64 0.003650 0.008621
|
|
65-69 0.013485 0.012500
|
|
70-74 0.014831 0.025172
|
|
75-79 0.025806 0.057692
|
|
|
|
, , Male, 3-5, Former
|
|
|
|
1-20 21+
|
|
45-49 0.001596 0.004175
|
|
50-54 0.004548 0.004889
|
|
55-59 0.007294 0.010258
|
|
60-64 0.013165 0.017901
|
|
65-69 0.023749 0.020810
|
|
70-74 0.044850 0.037129
|
|
75-79 0.077075 0.073298
|
|
|
|
, , Female, 3-5, Former
|
|
|
|
1-20 21+
|
|
45-49 0.002120 0.001786
|
|
50-54 0.002895 0.002701
|
|
55-59 0.003759 0.003610
|
|
60-64 0.006509 0.006996
|
|
65-69 0.012632 0.016880
|
|
70-74 0.012500 0.016873
|
|
75-79 0.025907 0.031250
|
|
|
|
, , Male, 6-10, Former
|
|
|
|
1-20 21+
|
|
45-49 0.002169 0.001226
|
|
50-54 0.003497 0.004029
|
|
55-59 0.005902 0.007440
|
|
60-64 0.012669 0.012207
|
|
65-69 0.018202 0.027664
|
|
70-74 0.038887 0.039888
|
|
75-79 0.049451 0.063830
|
|
|
|
, , Female, 6-10, Former
|
|
|
|
1-20 21+
|
|
45-49 0.001072 0.002247
|
|
50-54 0.002009 0.001902
|
|
55-59 0.001658 0.004545
|
|
60-64 0.004708 0.005417
|
|
65-69 0.008648 0.008287
|
|
70-74 0.011263 0.028487
|
|
75-79 0.039604 0.029787
|
|
|
|
, , Male, 11-15, Former
|
|
|
|
1-20 21+
|
|
45-49 0.001674 0.001983
|
|
50-54 0.002140 0.003939
|
|
55-59 0.004473 0.006685
|
|
60-64 0.008756 0.011000
|
|
65-69 0.016691 0.022681
|
|
70-74 0.031843 0.032686
|
|
75-79 0.056180 0.076661
|
|
|
|
, , Female, 11-15, Former
|
|
|
|
1-20 21+
|
|
45-49 0.001359 0.001421
|
|
50-54 0.001213 0.001168
|
|
55-59 0.002022 0.004122
|
|
60-64 0.005706 0.003731
|
|
65-69 0.005866 0.007979
|
|
70-74 0.010705 0.016212
|
|
75-79 0.016667 0.028037
|
|
|
|
, , Male, >=16, Former
|
|
|
|
1-20 21+
|
|
45-49 0.001595 0.001934
|
|
50-54 0.002504 0.003543
|
|
55-59 0.004366 0.005378
|
|
60-64 0.007030 0.009933
|
|
65-69 0.011592 0.012307
|
|
70-74 0.021949 0.024689
|
|
75-79 0.041289 0.050481
|
|
|
|
, , Female, >=16, Former
|
|
|
|
1-20 21+
|
|
45-49 0.000910 0.001388
|
|
50-54 0.001721 0.000830
|
|
55-59 0.002472 0.001821
|
|
60-64 0.003197 0.003564
|
|
65-69 0.006180 0.005815
|
|
70-74 0.012721 0.013634
|
|
75-79 0.018615 0.021954
|
|
|
|
>
|
|
> summary(smoke.rate[1:3,,1,,]) #test subscripting
|
|
Rate table with 4 dimensions:
|
|
age ranges from 45 to 55; with 3 categories
|
|
amount has levels of: 1-20 21+
|
|
duration ranges from 0 to 16; with 6 categories
|
|
status has levels of: Never Current Former
|
|
>
|
|
> proc.time()
|
|
user system elapsed
|
|
0.881 0.097 0.969
|