CircosHeatmap-aardio/dist/lib/r-library/survival/tests/expected.Rout.save

R Under development (unstable) (2023-01-09 r83585) -- "Unsuffered Consequences"
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> options(na.action=na.exclude) # preserve missings
> options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
> library(survival)
> 
> #  Tests of expected survival
> aeq <- function(x,y) all.equal(as.vector(x), as.vector(y))
> #
> # This makes several scripts easier
> # 
> mdy.Date <- function(m, d, y) {
+     y <- ifelse(y<100, y+1900, y)
+     as.Date(paste(m,d,y, sep='/'), "%m/%d/%Y")
+     }
> 
> # This function takes a single subject and walks down the rate table
> # Input: the vector of starting points, futime, and a ratetable
> # Output: the full history of walking through said table. Let n= #unique
> #   rates that were used
> #       cell = n by #dims of the table: index of the table cell
> #       days = time spent in cell
> #       hazard= accumulated hazard = days * rate
> # This does not do date or factor conversions -- start has to be numeric
> #
> ratewalk <- function(start, futime, ratetable=survexp.us) {
+     if (!is.ratetable(ratetable)) stop("Bad rate table")
+     ratedim <- dim(ratetable)
+     nvar <- length(ratedim)
+     if (length(start) != nvar) stop("Wrong length for start")
+     if (futime <=0) stop("Invalid futime")
+ 
+     attR <- attributes(ratetable)
+     discrete <- (attR$type ==1)  #discrete categories
+ 
+     maxn <- sum(!discrete)*prod(ratedim[!discrete]) #most cells you can hit
+     cell <- matrix(0, nrow=maxn, ncol=nvar)
+     days <- hazard <- double(maxn)
+ 
+     eps <- 1e-8  #Avoid round off error
+     n <- 0
+     while (futime >0) {
+         n <- n+1
+         #what cell am I in?
+         # Note that at the edges of the rate table, we use the edge: if
+         #   it only goes up the the year 2000, year 2000 is used for any
+         #   dates beyond.  This effectively eliminates one boundary
+         cell[n,discrete] <- start[discrete]
+         edge <- futime  #time to nearest edge, or finish
+         for (j in which(!discrete)) {
+             indx <- sum(start[j] >= attR$cutpoints[[j]]-eps)
+             cell[n, j] <- max(1, indx)
+             if (indx < ratedim[j]) 
+                 edge <- min(edge, (attR$cutpoints[[j]])[indx+1] - start[j])
+             }
+         days[n] <- edge  #this many days in the cell
+         # using a matrix as a subscript is so handy sometimes
+         hazard[n] <- edge * (as.matrix(ratetable))[cell[n,,drop=F]]
+         futime <- futime - edge  #amount of time yet to account for
+         start[!discrete] <- start[!discrete] + edge  #walk forward in time
+         }
+     list(cell=cell[1:n,], days=days[1:n], hazard=hazard[1:n])
+     }
> 
> # Simple test of ratewalk: 20 years old, start on 7Sep 1960
> #   116 days at the 1960, 20 year old male rate, through the end of the day
> #     on 12/31/1960, then 84 days at the 1961 rate.  
> #   The decennial q for 1960 males is .00169.
> zz <- ratewalk(c(20.4*365.25, 1, as.Date("1960/09/07")), 200)
> all.equal(zz$hazard[1], -(116/365.25)*log(1-.00169))
[1] TRUE
> all.equal(zz$days, c(116,84))
[1] TRUE
> 
>         
> #
> # Simple case 1: a single male subject, born 1/1/36 and entered on study 1/2/55
> #
> #  Compute the 1, 5, 10 and 12 year expected survival
> 
> temp1 <- mdy.Date(1,1,36)
> temp2 <- mdy.Date(1,2,55)
> exp1 <- survexp(~1,  ratetable=survexp.usr,times=c(366, 1827, 3653, 4383),
+                 rmap= list(year=temp2, age=(temp2-temp1), sex=1, race='white'))
> 
> t12 <- as.numeric(temp2-temp1)   # difftimes are a PITA
> h1 <- ratewalk(c(t12, 1, 1, temp2), 366,  survexp.usr)
> h2 <- ratewalk(c(t12, 1, 1, temp2), 1827, survexp.usr)
> h3 <- ratewalk(c(t12, 1, 1, temp2), 3653, survexp.usr)
> h4 <- ratewalk(c(t12, 1, 1, temp2), 4383, survexp.usr)
> 
> aeq(-log(exp1$surv), c(sum(h1$hazard), sum(h2$hazard), sum(h3$hazard),
+                        sum(h4$hazard)))
[1] TRUE
> 
> # pyears should give the same result
> dummy <- data.frame(time = 4383,
+                     year=temp2, sex = 1, age= temp2-temp1, race="white")
> cuts <- tcut(0, c(0, 366, 1827, 3653, 4383))
> exp1c <- pyears(time ~ cuts, data=dummy, ratetable=survexp.usr)
> aeq(exp1$surv, exp(-cumsum(exp1c$expected)))
[1] TRUE
> 
> 
> # Just a little harder: 
> #   Born 3/1/25 and entered the study on 6/10/55.  The code creates shifted
> #   dates to align with US rate tables - entry is 59 days earlier (days from
> #   1/1/1925 to 3/1/1925).
> #
> temp1 <- mdy.Date(3,1,25)
> temp2 <- mdy.Date(6,10,55)
> exp1 <- survexp(~1, ratetable=survexp.usr,times=c(366, 1827, 3653, 4383),
+                 rmap= list(year=temp2, age=(temp2-temp1), sex=2, race='black'))
> 
> tyear <- temp2 - 59
> t12 <-  as.numeric(temp2-temp1)
> h1 <- ratewalk(c(t12, 2, 2, tyear), 366,  survexp.usr)
> h2 <- ratewalk(c(t12, 2, 2, tyear), 1827, survexp.usr)
> h3 <- ratewalk(c(t12, 2, 2, tyear), 3653, survexp.usr)
> h4 <- ratewalk(c(t12, 2, 2, tyear), 4383, survexp.usr)
> 
> aeq(-log(exp1$surv), c(sum(h1$hazard), sum(h2$hazard), sum(h3$hazard),
+                        sum(h4$hazard)))
[1] TRUE
> 
> #
> # Simple case 2: make sure that the averages are correct, for Ederer method
> #
> #  Compute the 1, 5, 10 and 12 year expected survival
> 
> temp1 <- mdy.Date(1:6,6:11,1890:1895)
> temp2 <- mdy.Date(6:1,11:6,c(55:50))
> temp3 <- c(1,2,1,2,1,2)
> age <- temp2 - temp1
> 
> exp1 <- survexp(~1, rmap= list(year=temp2, age=(temp2-temp1), sex=temp3),
+ 		       times=c(366, 1827, 3653, 4383))
> exp2 <- survexp(~ I(1:6), 
+                 rmap= list(year=temp2, age=(temp2-temp1), sex=temp3),
+ 			times=c(366, 1827, 3653, 4383))
> exp3 <- exp2$surv
> for (i in 1:length(temp1)){
+     exp3[,i] <- survexp(~ 1,
+                         rmap = list(year=temp2, age=(temp2-temp1), sex=temp3),
+                         times=c(366, 1827, 3653, 4383), subset=i)$surv
+     }
> 
>              
> print(aeq(exp2$surv, exp3))
[1] TRUE
> print(all.equal(exp1$surv, apply(exp2$surv, 1, mean)))
[1] TRUE
> 
> # They agree, but are they right?
> #
> for (i in 1:length(temp1)) {
+     offset <- as.numeric(temp1[i] - mdy.Date(1,1, 1889+i))
+     tyear = temp2[i] - offset
+     haz1 <- ratewalk(c(as.numeric(temp2-temp1)[i], temp3[i], tyear), 366)
+     haz2 <- ratewalk(c(as.numeric(temp2-temp1)[i], temp3[i], tyear), 1827)
+     haz3 <- ratewalk(c(as.numeric(temp2-temp1)[i], temp3[i], tyear), 3653)
+     haz4 <- ratewalk(c(as.numeric(temp2-temp1)[i], temp3[i], tyear), 4383)
+     print(aeq(-log(exp2$surv[,i]), c(sum(haz1$hazard), sum(haz2$hazard),
+                                     sum(haz3$hazard), sum(haz4$hazard))))
+     }
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
[1] TRUE
> 
> #
> # Check that adding more time points doesn't change things
> #
> exp4 <- survexp(~ I(1:6),
+           rmap= list(year=temp2, age=(temp2-temp1), sex=temp3),
+ 		times=sort(c(366, 1827, 3653, 4383, 30*(1:100))))
> aeq(exp4$surv[match(exp2$time, exp4$time),], exp2$surv)
[1] TRUE
> 
> exp4 <- survexp(~1,
+                 rmap = list(year=temp2, age=(temp2-temp1), sex=temp3),
+ 		times=sort(c(366, 1827, 3653, 4383, 30*(1:100))))
> aeq(exp1$surv, exp4$surv[match(exp1$time, exp4$time, nomatch=0)])
[1] TRUE
> 
> 
> #
> # Now test Hakulinen's method, assuming an analysis date of 3/1/57
> #
> futime <- mdy.Date(3,1,57) - temp2
> xtime  <- sort(c(futime, 30, 60, 185, 365))
> 
> exp1 <- survexp(futime ~ 1, rmap= list(year=temp2, age=(temp2-temp1), sex=1),
+ 		times=xtime, conditional=F)
> exp2 <- survexp(~ I(1:6), times=futime, 
+                 rmap= list(year=temp2, age=(temp2-temp1), sex=1))
> 
> wt <- rep(1,6)
> con <- double(6)
> for (i in 1:6) {
+     con[i] <- sum(exp2$surv[i,i:6])/sum(wt[i:6])
+     wt <- exp2$surv[i,]
+     }
> 
> exp1$surv[match(futime, xtime)]
[1] 0.9557362 0.9285840 0.9025661 0.8774220 0.8532489 0.8297416
> aeq(exp1$surv[match(futime, xtime)], cumprod(con))
[1] TRUE
> 
> 
> #
> # Now for the conditional method
> #
> exp1 <- survexp(futime ~ 1, rmap= list(year=temp2, age=(temp2-temp1), sex=1),
+ 		times=xtime, conditional=T)
> 
> cond <- exp2$surv
> for (i in 6:2) cond[i,] <- (cond[i,]/cond[i-1,])  #conditional survival
> for (i in 1:6) con[i] <- exp(mean(log(cond[i, i:6])))
> 
> all.equal(exp1$surv[match(futime, xtime)], cumprod(con))
[1] TRUE
> cumprod(con)
[1] 0.9556656 0.9284398 0.9023612 0.8771798 0.8529944 0.8294940
> 
> #
> # Test out expected survival, when the parent pop is another Cox model
> #
> test1 <- data.frame(time=  c(4, 3,1,1,2,2,3),
+                     status=c(1,NA,1,0,1,1,0),
+                     x=     c(0, 2,1,1,1,0,0))
> 
> fit <- coxph(Surv(time, status) ~x, test1, method='breslow')
> 
> dummy <- data.frame(time=c(.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5),
+ 		    status=c(1,0,1,0,1,0,1,1,1), x=(-4:4)/2)
> 
> efit <- survexp(time ~ 1, rmap= list(x=x), dummy, ratetable=fit, cohort=F)
> 
> #
> # Now, compare to the true answer, which is known to us
> #
> ss <- exp(fit$coef)
> haz <- c( 1/(3*ss+3), 2/(ss+3), 1) #truth at time 0,1,2,4+
> chaz <- cumsum(c(0,haz))
> chaz2 <- chaz[c(1,2,2,3,3,3,3,4,4)]
> 
> risk <- exp(fit$coef*dummy$x)
> efit2 <- exp(-risk*chaz2)
> 
> all.equal(as.vector(efit), as.vector(efit2))  #ignore mismatched name attrib
[1] TRUE
> 
> #
> # Now test the direct-adjusted curve (Ederer)
> #
> efit <- survexp( ~ 1, dummy, ratetable=fit, se=F)
> direct <- survfit(fit, newdata=dummy, censor=FALSE)$surv
> 
> chaz <- chaz[-1]                  #drop time 0
> d2 <- exp(outer(-chaz, risk))
> all.equal(as.vector(direct), as.vector(d2))   #this tests survfit
[1] TRUE
> 
> all.equal(as.vector(efit$surv), as.vector(apply(direct,1,mean)))  #direct
[1] TRUE
> 
> # Check out the "times" arg of survexp
> efit2 <- survexp( ~1, dummy, ratetable=fit, se=F,
+                   times=c(.5, 2, 3.5,6))
> aeq(efit2$surv, c(1, efit$surv[c(2,2,3)]))
[1] TRUE
> 
> #
> # Now test out the Hakulinen method (Bonsel's method)
> #  By construction, we have a large correlation between x and censoring
> #
> # In theory, hak1 and hak2 would be the same.  In practice, like a KM and
> #   F-H, they differ when n is small.
> #
> efit <- survexp( time ~1, dummy, ratetable=fit, se=F)
> 
> surv  <- wt <- rep(1,9)
> tt <- c(1,2,4)
> hak1 <- hak2 <- NULL
> for (i in 1:3) {
+     wt[dummy$time < tt[i]]  <- 0
+     hak1 <- c(hak1,  exp(-sum(haz[i]*risk*surv*wt)/sum(surv*wt)))
+     hak2 <- c(hak2,  sum(exp(-haz[i]*risk)*surv*wt)/sum(surv*wt))
+     surv <- surv * exp(-haz[i]*risk)
+     }
> 
> all.equal(as.vector(efit$surv), as.vector(cumprod(hak1)))
[1] TRUE
> 
> #
> #  Now do the conditional estimate
> #
> efit <- survexp( time ~ 1, dummy, ratetable=fit, se=F,
+ 			conditional=T)
> wt <- rep(1,9)
> cond <- NULL
> for (i in 1:3) {
+     wt[dummy$time < tt[i]]  <- 0
+     cond <- c(cond,  exp(-sum(haz[i]*risk*wt)/sum(wt)))
+     }
> 
> all.equal(as.vector(efit$surv), as.vector(cumprod(cond)))
[1] TRUE
> 
> proc.time()
   user  system elapsed 
  1.083   0.077   1.150