Description Usage Arguments Value Author(s) References Examples
Colection of functions for survival models performance analysis.
R2sh
estimates a distance-based estimator of survival predictive accuracy proposed by Schemper and Henderson. It was inspirated in survAUC::schemper function, but receives the predicted values directly. Besides that, R2sh
does bootstrap resampling and returns its confidence interval estimate.
R2pm
calculates a estimator of survival predictive accuracy proposed by Kent & O'Quigley and its bootstrap confidence interval.
cal.Slope
returns the calibration slope of a survival model and its bootstrap confidence interval.
1 2 3 4 5 |
time |
A vector of event times. |
status |
A indicator vector of event occurrence. |
lin.pred |
A vector of linear predictors of a survival model for each observation. (prognostic index) |
data |
A data.frame where to find column vectors. |
R |
The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling, some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector of integers where each component gives the number of resamples from each of the rows of weights. To be passed to |
R2sh
returns a list with the following components:
D
: The estimator of predictive accuracy obtained from the covariate-free null model.
Dx
: The estimator of predictive accuracy obtained from the Cox model.
V
: The estimator of relative gains in predictive accuracy.
Mhat
: The absolute distance estimator obtained from the Cox model (evaluated at the event times of the test data).
Mhat.0
: The absolute distance estimator obtained from the covariate-free null model (evaluated at the event times of the test data).
timep
: The event times of the test data.
lower
: V lower confidence limit.
upper
: V upper confidence limit.
boot
: An object of class "boot
".
bootCI
: Boot confidence intervals resampling.
R2pm
returns a list with the following components:
r2
: The estimator of predictive accuracy obtained from the Cox model.
lower
: r2 lower confidence limit.
upper
: r2 upper confidence limit.
boot
: An object of class "boot
".
bootCI
: Boot confidence intervals resampling.
cal.Slope
returns a list with the following components:
slope
: The calibration slope measure of a survival model.
lower
: slope lower confidence limit.
upper
: slope upper confidence limit.
boot
: An object of class "boot
".
bootCI
: Boot confidence intervals resampling.
Lunna Borges <lunna.borges@epimedsolutions.com>
Schemper, M. and R. Henderson (2000). Predictive accuracy and explained variation in Cox regression. Biometrics 56, 249-255.
Davison, A.C. and Hinkley, D.V. (1997) Bootstrap Methods and Their Application, Chapter 5. Cambridge University Press.
DiCiccio, T.J. and Efron B. (1996) Bootstrap confidence intervals (with Discussion). Statistical Science, 11, 189-228.
Efron, B. (1987) Better bootstrap confidence intervals (with Discussion). Journal of the American Statistical Association, 82, 171-200.
Kent, John T., and J. O. H. N. O'QUIGLEY. "Measures of dependence for censored survival data." Biometrika 75.3 (1988): 525-534.
van Houwelingen, Hans C. "Validation, calibration, revision and combination of prognostic survival models." Statistics in medicine 19.24 (2000): 3401-3415.
Rahman, M. Shafiqur, et al. "Review and evaluation of performance measures for survival prediction models in external validation settings." BMC medical research methodology 17.1 (2017): 60.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | #### Survival model ####
data(breastCancer)
class(breastCancer$gradd1) <- "character"
class(breastCancer$gradd2) <- "character"
traindata <- breastCancer[sample(nrow(breastCancer), nrow(breastCancer)*2/3),]
newdata <- breastCancer[-sample(nrow(breastCancer), nrow(breastCancer)*2/3),]
model <- rms::cph(survival::Surv(rectime, censrec) ~ rms::rcs(age,6) +
rms::rcs(nodes,3) + rms::rcs(pgr,3) + gradd1 + gradd2 +
hormon, data = traindata)
lp <- predict(model, newdata = newdata)
#### R2sh example ####
R2sh(newdata$rectime, newdata$censrec, lp, data = newdata, R = 50)
#### R2pm example ####
R2pm(lp, R = 50)
#### cal.slope example ####
cal.Slope(newdata$rectime, newdata$censrec, lp, R = 50)
|
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