# Tests of pseudovalues, by calculating directly from survfit and residuals
#  this assumes that residuals.survfit is correct
library(survival)
aeq <- function(x, y, ...) all.equal(as.vector(x), as.vector(y), ...)

mdata <- mgus2
temp <- ifelse(mdata$pstat==1, 1, 2*mdata$death)
mdata$event <- factor(temp, 0:2, c("censor", "pcm", "death"))
mdata$etime <- ifelse(mdata$pstat==1, mdata$ptime, mdata$futime)
mdata <- subset(mdata, etime > 12)  # remove first year
tvec <- c(10, 100, 200, 365)

# Single endpoint, one curve
fit1 <- survfit(Surv(ptime, pstat) ~1, mdata)
# a time point before first event, after last event, at an event time,
#  and between event times
rr1 <- resid(fit1, tvec)
aeq(colSums(rr1), rep(0,4))
sv1 <- summary(fit1, time=tvec, extend=TRUE)$surv

# one time point  
ps1a <- pseudo(fit1, time=100)
aeq(ps1a, sv1[2] + fit1$n*rr1[,2])
# multiple
ps1b <- pseudo(fit1,  time=tvec)
aeq(ps1b,  sv1[col(rr1)] + fit1$n * rr1)

# Single endpoint, multiple curves
fit2 <- survfit(Surv(futime, death) ~ sex, mdata)
rr2 <- resid(fit2, time=tvec)
aeq(colSums(rr2), rep(0,4))
sv2 <- summary(fit2, time=tvec, extend=TRUE)$surv
sv2 <- t(matrix(sv2, ncol=2))   # row 1= female, row2 = male

# residuals are the same as for separate models
fit2a <- survfit(Surv(futime, death) ~1, mdata, subset=( sex=='F'))
fit2b <- survfit(Surv(futime, death) ~1, mdata, subset= (sex=='M'))
fem <- (mdata$sex=='F')
rr2a <- resid(fit2a, times=tvec)
rr2b <- resid(fit2b, times=tvec)
aeq(rr2a, rr2[fem,])  # row names won't be equal
aeq(rr2b, rr2[!fem,])

# one time point
ps2a <- pseudo(fit2a, time=100)
aeq(ps2a, sv2[1,2] + fit2a$n[1]* rr2a[,2])
ps2b <- pseudo(fit2b, time=100)
aeq(ps2b, sv2[2,2] + fit2b$n[1]* rr2b[,2])

# overall psuedo are the same as for separate models
#  (each row of mdata belongs to a single curve)
ps2c <- pseudo(fit2, time=100)
aeq(ps2c[ fem], ps2a)
aeq(ps2c[!fem], ps2b)

# multiple time points
ps2d <- pseudo(fit2a, times=tvec)
aeq(ps2d, sv2[1, col(rr2a)] + fit2$n[1]* rr2a)
ps2e <- pseudo(fit2b, times=tvec)
aeq(ps2e, sv2[2, col(rr2b)] + fit2$n[2]* rr2b)

ps2f <- pseudo(fit2, times=tvec)
aeq(ps2d, ps2f[ fem,])
aeq(ps2e, ps2f[!fem,])

# Repeat the process for a multi-state model
fit3 <- survfit(Surv(etime, event) ~ sex, mdata)
fit3a <- survfit(Surv(etime, event) ~1, mdata, subset= (sex=='F'))
fit3b <- survfit(Surv(etime, event) ~1, mdata, subset= (sex=='M'))
rr3 <-  resid(fit3, times=tvec)
aeq(apply(rr3, 2:3, sum), matrix(0,3,4)) # resids sum to 0 for each state & time
rr3a <- resid(fit3a, times=tvec)
rr3b <- resid(fit3b, times=tvec)
aeq(rr3[fem,,], rr3a)
aeq(rr3[!fem,,], rr3b)

ps3 <- pseudo(fit3, times=tvec)
ps3a <- pseudo(fit3a, times=tvec)
ps3b <- pseudo(fit3b, times=tvec)
aeq(ps3[ fem,,], ps3a)
aeq(ps3[!fem,,], ps3b)

sv3 <- summary(fit3, times=tvec, extend=TRUE)$pstate
sv3 <- array(sv3, dim=c(4,2,3))      #times, curve, order
# ps3a has dimensions (number obs in fit3a, 3 states, 4 timepoints)
#  to each of the 3x4 combinations we need to add the value of the
#  survival curve at that time.  A loop is easiest
temp1 <- array(0, dim= dim(rr3a))
temp2 <- array(0, dim= dim(rr3b))
for (i in 1:3) { # each of the 3 states
    for (j in 1:4) {  # each of the 4 times 
        temp1[, i,j] <- sv3[j,1,i] + fit3$n[1]*rr3a[,i,j]
        temp2[, i,j] <- sv3[j,2,i] + fit3$n[2]*rr3b[,i,j]
    }
}
aeq(temp1, ps3a)
aeq(temp2, ps3b)

###########################
# All again, just the same, for cumulative hazards
#  Though there are 2 of them, vs 3 states.
#
rr1 <- resid(fit1, tvec, type="cumhaz")
aeq(colSums(rr1), rep(0,4))
sv1 <- summary(fit1, time=tvec, extend=TRUE)$cumhaz

# one time point  
ps1a <- pseudo(fit1, time=100, type="cumhaz")
aeq(ps1a, sv1[2] + fit1$n*rr1[,2])
# multiple
ps1b <- pseudo(fit1,  time=tvec, type="cumhaz")
aeq(ps1b,  sv1[col(rr1)] + fit1$n * rr1)

# Single endpoint, multiple curves
fit2 <- survfit(Surv(futime, death) ~ sex, mdata)
rr2 <- resid(fit2, time=tvec, type="cumhaz")
aeq(colSums(rr2), rep(0,4))
sv2 <- summary(fit2, time=tvec, extend=TRUE)$cumhaz
sv2 <- t(matrix(sv2, ncol=2))   # row 1= female, row2 = male

# residuals are the same as for separate models
rr2a <- resid(fit2a, times=tvec, type= "cumhaz")
rr2b <- resid(fit2b, times=tvec, type= "cumhaz")
aeq(rr2a, rr2[fem,])
aeq(rr2b, rr2[!fem,])

# one time point
ps2a <- pseudo(fit2a, time=100, type="cumhaz")
aeq(ps2a, sv2[1,2] + fit2a$n[1]* rr2a[,2])
ps2b <- pseudo(fit2b, time=100, type="cumhaz")
aeq(ps2b, sv2[2,2] + fit2b$n[1]* rr2b[,2])

# overall psuedo are the same as for separate models
#  (each row of mdata belongs to a single curve)
ps2c <- pseudo(fit2, time=100, type="cumhaz")
aeq(ps2c[ fem], ps2a)
aeq(ps2c[!fem], ps2b)

# multiple time points
ps2d <- pseudo(fit2a, times=tvec, type="cumhaz")
aeq(ps2d, sv2[1, col(rr2a)] + fit2$n[1]* rr2a)
ps2e <- pseudo(fit2b, times=tvec, type= "cumhaz")
aeq(ps2e, sv2[2, col(rr2b)] + fit2$n[2]* rr2b)

ps2f <- pseudo(fit2, times=tvec, type="cumhaz")
aeq(ps2d, ps2f[ fem,])
aeq(ps2e, ps2f[!fem,])

# Repeat the process for a multi-state model
rr3 <-  resid(fit3, times=tvec, type="cumhaz")
aeq(apply(rr3, 2:3, sum), matrix(0, 2,4))
rr3a <- resid(fit3a, times=tvec, type="cumhaz")
rr3b <- resid(fit3b, times=tvec, type="cumhaz")
aeq(rr3[fem,,], rr3a)
aeq(rr3[!fem,,], rr3b)

ps3 <- pseudo(fit3, times=tvec, type="cumhaz")
ps3a <- pseudo(fit3a, times=tvec, type="cumhaz")
ps3b <- pseudo(fit3b, times=tvec, type="cumhaz")
aeq(ps3[ fem,,], ps3a)
aeq(ps3[!fem,,], ps3b)

sv3 <- summary(fit3, times=tvec, extend=TRUE)$cumhaz
sv3 <- array(sv3, dim=c(4,2,2))      #times, curve, hazard
# ps3a has dimensions (number obs in fit3a, 4 timepoints, 3 states)
#  to each of the 4x3 combinations we need to add the value of the
#  survival curve at that time.  A loop is easiest
temp1 <- array(0, dim= dim(rr3a))
temp2 <- array(0, dim= dim(rr3b))
for (i in 1:2) { # each of the 2 hazard
    for (j in 1:4) {  # each of the 4 timepoints
        temp1[, i,j] <- sv3[j,1,i] + fit3$n[1]*rr3a[,i,j]
        temp2[, i,j] <- sv3[j,2,i] + fit3$n[2]*rr3b[,i,j]
    }
}
aeq(temp1, ps3a)
aeq(temp2, ps3b)

#################################################
# Last, one more time with AUC
#  A bit more bother, since summary.survfit only returns AUC for one time
#   value at a time.  It also does not like times before the first event
#
tvec <- tvec[2:4]

rr1 <- resid(fit1, tvec, type="auc")
aeq(colSums(rr1), rep(0,3))
afun <- function(fit, times) {
    ntime <- length(times)
    if (length(fit$strata)) xfun <- function(x) x$table[, "rmean"]
        else xfun <- function(x) x$table["rmean"]

    temp <- xfun(summary(fit, rmean=times[1]))
    if (ntime==1) return(temp)
    
    result <- matrix(0, ntime, length(temp))
    result[1,] <- temp
    for (i in 2:ntime) 
        result[i,] <- xfun(summary(fit, rmean=times[i]))
    drop(result)
}
    
sv1 <- afun(fit1, tvec)

# one time point  
ps1a <- pseudo(fit1, time=tvec[1], type="auc")
aeq(ps1a, sv1[1] + fit1$n*rr1[,1])
# multiple
ps1b <- pseudo(fit1,  time=tvec, type="auc")
aeq(ps1b,  sv1[col(rr1)] + fit1$n * rr1)

# Single endpoint, multiple curves
rr2 <- resid(fit2, time=tvec, type="auc")
sv2 <- t(afun(fit2, tvec))
aeq(colSums(rr2), rep(0,3))

# residuals are the same as for separate models
rr2a <- resid(fit2a, times=tvec, type= "auc")
rr2b <- resid(fit2b, times=tvec, type= "auc")
aeq(rr2a, rr2[fem,])
aeq(rr2b, rr2[!fem,])

# one time point
ps2a <- pseudo(fit2a, time=100, type="auc")
aeq(ps2a, sv2[1,1] + fit2a$n[1]* rr2a[,1])
ps2b <- pseudo(fit2b, time=100, type="auc")
aeq(ps2b, sv2[2,1] + fit2b$n[1]* rr2b[,1])

# overall psuedo are the same as for separate models
#  (each row of mdata belongs to a single curve)
ps2c <- pseudo(fit2, time=100, type="auc")
aeq(ps2c[ fem], ps2a)
aeq(ps2c[!fem], ps2b)

# multiple time points
ps2d <- pseudo(fit2a, times=tvec, type="auc")
aeq(ps2d, sv2[1, col(rr2a)] + fit2$n[1]* rr2a)
ps2e <- pseudo(fit2b, times=tvec, type= "auc")
aeq(ps2e, sv2[2, col(rr2b)] + fit2$n[2]* rr2b)

ps2f <- pseudo(fit2, times=tvec, type="auc")
aeq(ps2d, ps2f[ fem,])
aeq(ps2e, ps2f[!fem,])

# Repeat the process for a multi-state model
rr3 <-  resid(fit3, times=tvec, type="auc")
aeq(apply(rr3, 2:3, sum), matrix(0, 3,3))
rr3a <- resid(fit3a, times=tvec, type="auc")
rr3b <- resid(fit3b, times=tvec, type="auc")
aeq(rr3[fem,,], rr3a)
aeq(rr3[!fem,,], rr3b)

ps3 <- pseudo(fit3, times=tvec, type="auc")
ps3a <- pseudo(fit3a, times=tvec, type="auc")
ps3b <- pseudo(fit3b, times=tvec, type="auc")
aeq(ps3[ fem,,], ps3a)
aeq(ps3[!fem,,], ps3b)

sv3 <- rbind(summary(fit3, rmean=tvec[1])$table[,"rmean"],
             summary(fit3, rmean=tvec[2])$table[,"rmean"],
             summary(fit3, rmean=tvec[3])$table[,"rmean"])
sv3 <- array(sv3, dim=c(3,2,3))      #times, curve, state
# ps3a has dimensions (number obs in fit3a, 4 timepoints, 3 states)
#  to each of the 4x3 combinations we need to add the value of the
#  survival curve at that time.  A loop is easiest
temp1 <- array(0, dim= dim(rr3a))
temp2 <- array(0, dim= dim(rr3b))
for (i in 1:3) { # each of the 3 states
    for (j in 1:3) {  # each of the 3 times
        temp1[, i,j] <- sv3[j,1,i] + fit3$n[1]*rr3a[,i,j]
        temp2[, i,j] <- sv3[j,2,i] + fit3$n[2]*rr3b[,i,j]
    }
}
aeq(temp1, ps3a)
aeq(temp2, ps3b)

#
# a data set with a missing value, and with a group that has only one obs
#  a good test of edge cases
#
lfit1 <- survfit(Surv(time, status) ~ ph.ecog, lung)
# This will warn about points beyond the curve; ph.ecog==3 has a single point
# at time=118, and it will have one fewer obs than the data
p1 <- pseudo(lfit1, times=c(100, 200))
aeq(dim(p1), c(nrow(lung)-1, 2))


# This will have rows that match the data
lfit2 <- survfit(Surv(time, status) ~ ph.ecog, lung, na.action= na.exclude)
p2 <- pseudo(lfit2, time=c(100, 200))
aeq(dim(p2), c(nrow(lung), 2))
all(is.na(p2[is.na(lung$ph.ecog)]))  # a row of missing was inserted

row3 <- which(!is.na(lung$ph.ecog) & lung$ph.ecog ==3)  # the singleton row
all(p2[row3,] == c(1, 0))