# val.surv

From rms v2.0-2
0th

Percentile

##### Validate Predicted Probabilities Against Observed Survival Times

The val.surv function is useful for validating predicted survival probabilities against right-censored failure times.

val.surv uses Cox-Snell (1968) residuals on the cumulative probability scale to check on the calibration of a survival model against right-censored failure time data. If the predicted survival probability at time $t$ for a subject having predictors $X$ is $S(t|X)$, this method is based on the fact that the predicted probability of failure before time $t$, $1 - S(t|X)$, when evaluated at the subject's actual survival time $T$, has a uniform (0,1) distribution. The quantity $1 - S(T|X)$ is right-censored when $T$ is. By getting one minus the Kaplan-Meier estimate of the distribution of $1 - S(T|X)$ and plotting against the 45 degree line we can check for calibration accuracy. A more stringent assessment can be obtained by stratifying this analysis by an important predictor variable. The theoretical uniform distribution is only an approximation when the survival probabilities are estimates and not population values.

When censor is specified to val.surv, a different validation is done that is more stringent but that only uses the uncensored failure times. This method is used for type I censoring when the theoretical censoring times are known for subjects having uncensored failure times. Let $T$, $C$, and $F$ denote respectively the failure time, censoring time, and cumulative failure time distribution ($1 - S$). The expected value of $F(T | X)$ is 0.5 when $T$ represents the subject's actual failure time. The expected value for an uncensored time is the expected value of $F(T | T \leq C, X) = 0.5 F(C | X)$. A smooth plot of $F(T|X) - 0.5 F(C|X)$ for uncensored $T$ should be a flat line through $y=0$ if the model is well calibrated. A smooth plot of $2F(T|X)/F(C|X)$ for uncensored $T$ should be a flat line through $y=1.0$. The smooth plot is obtained by smoothing the (linear predictor, difference or ratio) pairs.

Keywords
models, regression, smooth, survival
##### Usage
val.surv(fit, newdata, S, est.surv, censor)## S3 method for class 'val.surv':
plot(x, group, g.group=4,
what=c('difference','ratio'),
type=c('l','b','p'),
xlab, ylab, xlim, ylim, datadensity=TRUE, ...)
##### Arguments
fit
a fit object created by cph or psm
newdata
a data frame for which val.surv should obtain predicted survival probabilities. If omitted, survival estimates are made for all of the subjects used in fit.
S
an Surv object
est.surv
a vector of estimated survival probabilities corresponding to times in the first column of S.
censor
a vector of censoring times. Only the censoring times for uncensored observations are used.
x
result of val.surv
group
a grouping variable. If numeric this variable is grouped into g.group quantile groups (default is quartiles).
g.group
number of quantile groups to use when group is given and variable is numeric.
what
the quantity to plot when censor was in effect. The default is to show the difference between cumulative probabilities and their expectation given the censoring time. Set what="ratio" to show the ratio instead.
type
Set to the default ("l") to plot the trend line only, "b" to plot both individual subjects ratios and trend lines, or "p" to plot only points.
xlab
x-axis label
ylab
y-axis label
xlim
ylim
axis limits for plot.val.surv when the censor variable was used.
By default, plot.val.surv will show the data density on each curve that is created as a result of censor being present. Set datadensity=FALSE to suppress these tick marks drawn by scat1d.
...
optional arguments for plsmo or plot
##### Value

• a list of class "val.surv"

##### concept

• model validation
• predictive accuracy

##### References

Stallard N (2009): Simple tests for th external validation of mortality prediction scores. Stat in Med 28:377--388.

Cox DR, Snell EJ (1968):A general definition of residuals (with discussion). JRSSB 30:248--275.

May M, Royston P, Egger M, Justice AC, Sterne JAC (2004):Development and validation of a prognostic model for survival time data: application to prognosis of HIV positive patients treated with antiretroviral therapy. Stat in Med 23:2375--2398.

validate, calibrate, scat1d, cph, psm

##### Aliases
• val.surv
• plot.val.surv
##### Examples
# Generate failure times from an exponential distribution
set.seed(123)              # so can reproduce results
n <- 2000
age <- 50 + 12*rnorm(n)
sex <- factor(sample(c('Male','Female'), n, rep=TRUE, prob=c(.6, .4)))
cens <- 15*runif(n)
h <- .02*exp(.04*(age-50)+.8*(sex=='Female'))
t <- -log(runif(n))/h
label(t) <- 'Time to Event'
ev <- ifelse(t <= cens, 1, 0)
t <- pmin(t, cens)
S <- Surv(t, ev)

# First validate true model used to generate data
w <- val.surv(est.surv=exp(-h*t), S=S)
plot(w)
plot(w, group=sex)  # stratify by sex

# Now fit an exponential model and validate
# Note this is not really a validation as we're using the
# training data here
f <- psm(S ~ age + sex, dist='exponential', y=TRUE)
w <- val.surv(f)
plot(w, group=sex)

# We know the censoring time on every subject, so we can
# compare the predicted Pr[T <= observed T | T>c, X] to
# its expectation 0.5 Pr[T <= C | X] where C = censoring time
# We plot a ratio that should equal one
w <- val.surv(f, censor=cens)
plot(w)
plot(w, group=age, g=3)   # stratify by tertile of age
Documentation reproduced from package rms, version 2.0-2, License: GPL (>= 2)

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