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predict.R
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predict.surrosurv <- function(object,
models = names(object),
exact.models,
...) {
if (missing(exact.models))
exact.models <- all(sapply(tolower(noSpP(models)), function(m)
any(tolower(noSpP(
names(object)
)) %in% m)))
if (exact.models) {
ind <-
which(tolower(noSpP(names(object))) %in% tolower(noSpP(models)))
} else {
ind <- which(sapply(tolower(noSpP(names(
object
))), function(mod)
! all(is.na(
pmatch(tolower(noSpP(models)), mod)
))))
}
models <- names(object)[ind]
copulas <- intersect(c('Clayton', 'Plackett', 'Hougaard'), models)
poissons <- grep('PoissonT', models, value = TRUE)
allRES <- c(# Copula models
sapply(copulas, function(cop) {
list(unadj = as.data.frame(object[[cop]]$unadj$step1[c('alpha', 'beta')]),
adj = as.data.frame(object[[cop]]$adj$ranef))
}),
# Poisson models
lapply(poissons, function(poi) {
# object[[poi]]$ranef$trialref[, c('trtS', 'trtT')]
as.data.frame(t(t(object[[poi]]$ranef$trialref[, c('trtS', 'trtT')]) +
unlist(object[[poi]][c('alpha', 'beta')])))
}))
allRES <- lapply(allRES, function(object) {
colnames(object) <- c('trtS', 'trtT')
return(object)
})
names(allRES) <- c(paste(rep(copulas, each = 2), rep(c('unadj', 'adj'), length(copulas)), sep =
'.'),
poissons)
class(allRES) <- c('predictSurrosurv', class(allRES))
attr(allRES, 'trialSizes') <- attr(object, 'trialSizes')
attr(allRES, 'predf') <- c(# Copula models
sapply(copulas, function(cop) {
list(unadj = Vectorize(function(x) {
# est <- coef(object[[cop]]$unadj$step2) %*% c(1, x)
# n <- 2 + object[[cop]]$unadj$step2$df.residual
# t <- qt(c(.025, .975), n - 2)
# S <- summary(object[[cop]]$unadj$step2)$sigma
# sqrt(
# 1 + 1 / n + n * (
# x - mean(object[[cop]]$unadj$step1$alpha)
# )^2 / (
# n * sum(object[[cop]]$unadj$step1$alpha^2) -
# sum(object[[cop]]$unadj$step1$alpha)^2
# )
# ) * S * t + est
res <- as.vector(predict(object[[cop]]$unadj$step2,
data.frame(alpha = x),
interval = 'prediction'))
names(res) <- c('fit', 'lwr', 'upr')
return(res)
}),
adj = Vectorize(function(x) {
cMEAN <- object[[cop]]$adj$step2$gamma %*% c(1, x)
# The following variance is given by equation (18.27), page 332
# Burzykowski and Buyse, An alternative measure for surrogate
# endpoint validation, in Burzykowski, Molenberghs, Buyse, 2005
# The evaluation of surrogate endpoints, New York Springer
# library(Matrix)
pdVCOV <- nearPD(object[[cop]]$adj$step2$gamma.vcov)$mat
pVAR <-
pdVCOV[1, 1] + 2 * x * pdVCOV[1, 2] + x ^ 2 * pdVCOV[2, 2] +
object[[cop]]$adj$step2$sigma
res <- rep(cMEAN, 3) +
qnorm(c(
fit = .5,
lwr = .025,
upr = .975
)) * sqrt(pVAR)
names(res) <- c('fit', 'lwr', 'upr')
return(res)
}))
}),
# Poisson models
lapply(poissons, function(poi) {
Vectorize(function(x) {
# VCOV <- as.matrix(
# object[[poi]]$VarCor$trialref[paste0('trt', c('S', 'T')),
# paste0('trt', c('S', 'T'))] +
# object[[poi]]$fixVarCor[paste0('trt', c('S', 'T')),
# paste0('trt', c('S', 'T'))])
# CORR <- VCOV[1, 2] / sqrt(prod(diag(VCOV)))
# AVG <- unlist(object[[poi]][c('alpha', 'beta')])
# cMEAN <- AVG['beta.trtT'] + (x - AVG['alpha.trtS']) * CORR *
# # sqrt(VCOV['trtT', 'trtT'] / VCOV['trtS', 'trtS'])
# sqrt(exp(diff(log(diag(VCOV)))))
# res <- as.vector(
# rep(cMEAN, 3) + qnorm(c(fit=.5, lwr=.025, upr=.975)) *
# sqrt(VCOV['trtS', 'trtS'] * (1 - VCOV[1, 2] /
# prod(sqrt(diag(VCOV))))))
cMEAN <- object[[poi]]$regr$gamma %*% c(1, x)
# The following variance is given by equation (18.27), page 332
# Burzykowski and Buyse, An alternative measure for surrogate
# endpoint validation, in Burzykowski, Molenberghs, Buyse, 2005
# The evaluation of surrogate endpoints, New York Springer
# library(Matrix)
pdVCOV <- nearPD(object[[poi]]$regr$gamma.vcov)$mat
pVAR <-
pdVCOV[1, 1] + 2 * x * pdVCOV[1, 2] + x ^ 2 * pdVCOV[2, 2] +
object[[poi]]$regr$sigma
res <- rep(cMEAN, 3) +
qnorm(c(
fit = .5,
lwr = .025,
upr = .975
)) * sqrt(pVAR)
names(res) <- c('fit', 'lwr', 'upr')
return(res)
})
}))
attr(allRES, 'surro.stats') <- print(object, silent = TRUE)
attr(allRES, 'surro.stats') <- attr(allRES, 'surro.stats')[noSpP(rownames(attr(allRES, 'surro.stats'))) %in%
noSpP(names(allRES)), , drop = FALSE]
noSpP(names(allRES))
names(attr(allRES, 'predf')) <- names(allRES)
return(allRES)
}
format.methodNames <- function(x) {
sub(
'.unadj',
' copula (Unadjusted)',
sub(
'.adj',
' copula (Adjusted)',
sub('Poisson', 'Poisson ', names(x)),
fixed = TRUE
),
fixed = TRUE
)
}
noSpP <- function(x)
gsub('[\\. ]', '', x)
print.predictSurrosurv <- function(x, n = 6, ...) {
cat('Treatment effect prediction for surrosurv object\n')
for (i in 1:length(x)) {
method <- format.methodNames(x)[i]
cat('\n ', method, '\n')
res2print <- format(t(head(x[[i]], n)), digits = 1)
if (nrow(x[[i]] > n))
res2print <- cbind(res2print, ' ' = c('...', '...'))
rownames(res2print) <- paste0(sub('trt', ' Treatment effects on ', rownames(res2print)), ':')
print(res2print, quote = FALSE, ...)
}
}
##############################################################################
ste <- function(x, models = names(x), exact.models) {
if (class(x)[1] == 'surrosurv')
x <- predict(x)
if (missing(exact.models))
exact.models <- all(sapply(tolower(noSpP(models)), function(m)
any(tolower(noSpP(
names(x)
)) %in% m)))
if (exact.models) {
ind <- which(tolower(noSpP(names(x))) %in% tolower(noSpP(models)))
} else {
ind <- which(sapply(tolower(noSpP(names(
x
))), function(mod)
! all(is.na(
pmatch(tolower(noSpP(models)), mod)
))))
}
res <- sapply(ind, function(i) {
f <- function(y)
attr(x, 'predf')[[i]](y)['upr', ] ^ 2
ste <- optimize(f, c(-1e8, 1e8))$minimum
ste <- ifelse(abs(f(ste)) > 1e-4, NA, ste)
return(ste)
})
names(res) <- names(x)[ind]
class(res) <- 'steSurrosurv'
return(res)
}
print.steSurrosurv <- function(x, digits = 2, ...) {
res2print <- cbind(beta = round(x, digits),
HR = round(exp(x), digits))
print(res2print, quote = FALSE, ...)
}
##############################################################################
plot.surrosurv <- function(x, ...) {
plot(predict(x), ...)
}
plot.predictSurrosurv <- function(x,
models = names(x),
exact.models,
pred.ints = TRUE,
show.ste = TRUE,
surro.stats = TRUE,
xlab,
ylab,
xlim,
ylim,
mfrow,
main,
...) {
# ************************************************************************** #
if (missing(xlab))
xlab <- 'Treatment effect (HR) on S'
if (missing(ylab))
ylab <- 'Treatment effect (HR) on T'
if (missing(exact.models))
exact.models <- all(sapply(tolower(noSpP(models)), function(m)
any(tolower(noSpP(
names(x)
)) %in% m)))
w <- attr(x, 'trialSizes')
if (var(w)) {
w <- .8 + 3 * (w - min(w)) / (max(w) - min(w))
} else
w <- 1
if (exact.models) {
ind <- which(tolower(noSpP(names(x))) %in% tolower(noSpP(models)))
} else {
ind <- which(sapply(tolower(noSpP(names(
x
))), function(mod)
! all(is.na(
pmatch(tolower(noSpP(models)), mod)
))))
}
PREDF <- attr(x, 'predf')[ind]
if (show.ste)
STE <- ste(x, names(x)[ind], exact.models = TRUE)
if (surro.stats) {
SURRO.STATS <-
round(matrix(as.numeric(attr(
x, 'surro.stats'
)), ncol = 2),
2)[ind, , drop = FALSE]
SURRO.STATS[is.na(SURRO.STATS)] <- '\u2014'
}
x <- x[ind]
if (!missing('xlim'))
xlims <- log(xlim)
if (!missing('ylim'))
ylims <- log(ylim)
if (length(x)) {
if (missing(mfrow)) {
par(mfrow = n2mfrow(length(x)))
} else {
par(mfrow = mfrow)
}
for (i in 1:length(x)) {
# abcoeff <- try(coef(lm(x[[i]][, 2:1])), silent = TRUE)
predf.fit <- Vectorize(function(y) {
PREDF[[i]](y)[1]
})
set0in <- function(xint) {
if (xint[1] > 0)
xint[1] <- 0
if (xint[2] < 0)
xint[2] <- 0
five <- diff(range(xint)) / 20
return(xint + c(-1, 1) * five)
}
if (missing('xlim'))
xlims <- set0in(range(x[[i]][, 1]))
if (missing('ylim'))
ylims <- set0in(range(x[[i]][, 2]))
plot(
x[[i]],
asp = 1,
xlim = xlims,
ylim = ylims,
cex = w,
pch = 21,
bg = rgb(.5, .5, .5, .5),
panel.first = {
if (pred.ints) {
Xs <- seq(axTicks(1)[1] - 1, rev(axTicks(1))[1] + 1,
length.out = 1e2)
polygon(
c(Xs, rev(Xs)),
c(PREDF[[i]](Xs)[2,], rev(PREDF[[i]](Xs)[3,])),
col = rgb(.8, .8, .8, .75),
border = NA
)
}
abline(h = 0, v = 0, col = 'grey')
# if (all(is.finite(abcoeff)))
# abline(lm(x[[i]][, 2:1]), col=rgb(.2, .2, .2, .8), lwd = 2)
curve(
predf.fit,
from = axTicks(1) - 1,
to = rev(axTicks(1))[1] + 1,
col = rgb(.2, .2, .2, .8),
lwd = 2,
add = TRUE
)
},
xaxt = 'n',
yaxt = 'n',
main = ifelse(missing(main), format.methodNames(x)[i], main),
xlab = xlab,
ylab = ylab,
...
)
if (show.ste) {
if (is.na(STE[i])) {
message(paste0(
'[', format.methodNames(x)[i],
'] The STE does not exist: the upper limits ',
'of the prediction intervals never cross the line HR=1'))
} else {
points(STE[i],
0,
col = 2,
pch = '|',
font = 2)
text(
STE[i],
0,
col = 2,
font = 2,
adj = c(1.1, -.5),
labels = paste('STE =', round(exp(STE[i]), 2))
)
}
}
if (surro.stats) {
mtext(
bquote(paste(R ^ 2, ' = ', .(
SURRO.STATS[i, 2]
))),
side = 1,
line = -1.2,
adj = .9,
font = 2
)
mtext(
bquote(paste(tau, ' = ', .(
SURRO.STATS[i, 1]
))),
side = 1,
line = -2.8,
adj = .9,
font = 2
)
}
axis(1,
at = axTicks(1),
labels = sapply(exp(axTicks(1)), function(x)
ifelse(
x < .1,
format(x, nsmall = 3, digits = 2),
ifelse(
x < 1,
format(x, nsmall = 2, digits = 2),
ifelse(
x < 10,
format(x, nsmall = 1, digits = 2),
format(x, digits = 2, drop0trailing =
FALSE)
)
)
)))
axis(2,
at = axTicks(2),
labels = sapply(exp(axTicks(2)), function(x)
ifelse(
x < .1,
format(x, nsmall = 3, digits = 2),
ifelse(
x < 1,
format(x, nsmall = 2, digits = 2),
ifelse(
x < 10,
format(x, nsmall = 1, digits = 2),
format(x, digits = 2, drop0trailing =
FALSE)
)
)
)))
}
} else
warning('No model selected', immediate. = TRUE)
}