predictCoxPL | R Documentation |
Same as predictCox except that the survival is estimated using the product limit estimator.
predictCoxPL( object, times, newdata = NULL, type = c("cumhazard", "survival"), keep.strata = TRUE, keep.infoVar = FALSE, ... )
object |
The fitted Cox regression model object either
obtained with |
times |
[numeric vector] Time points at which to return the estimated hazard/cumulative hazard/survival. |
newdata |
[data.frame or data.table] Contain the values of the predictor variables
defining subject specific predictions.
Should have the same structure as the data set used to fit the |
type |
[character vector] the type of predicted value. Choices are
Several choices can be
combined in a vector of strings that match (no matter the case)
strings |
keep.strata |
[logical] If |
keep.infoVar |
[logical] For internal use. |
... |
additional arguments to be passed to |
Note: the iid and standard errors are computed using the exponential approximation.
library(survival) #### generate data #### set.seed(10) d <- sampleData(40,outcome="survival") nd <- sampleData(4,outcome="survival") d$time <- round(d$time,1) #### Cox model #### fit <- coxph(Surv(time,event)~ X1 + X2 + X6, data=d, ties="breslow", x = TRUE, y = TRUE) ## exponential approximation predictCox(fit, newdata = d, times = 1:5) ## product limit predictCoxPL(fit, newdata = d, times = 1:5) #### stratified Cox model #### fitS <- coxph(Surv(time,event)~ X1 + strata(X2) + X6, data=d, ties="breslow", x = TRUE, y = TRUE) ## exponential approximation predictCox(fitS, newdata = d, times = 1:5) ## product limit predictCoxPL(fitS, newdata = d, times = 1:5) #### fully stratified Cox model #### fitS <- coxph(Surv(time,event)~ 1, data=d, ties="breslow", x = TRUE, y = TRUE) ## product limit GS <- survfit(Surv(time,event)~1, data = d) range(predictCoxPL(fitS)$survival - GS$surv) fitS <- coxph(Surv(time,event)~ strata(X2), data=d, ties="breslow", x = TRUE, y = TRUE) ## product limit GS <- survfit(Surv(time,event)~X2, data = d) range(predictCoxPL(fitS)$survival - GS$surv)
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