predict.HR: predict method for an object of class 'HR'.

Description Usage Arguments Value Author(s) References See Also Examples

Description

predict method for an object of class 'HR'.

Usage

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## S3 method for class 'HR'
predict(object, predictor, prob=NULL, pred.value=NULL, conf.level=0.95,
prediction.values=NULL, round.x=NULL, ref.label=NULL, ...)

Arguments

object

An object of class HR.

predictor

Variable named in the formula or included as a predictor in the coxfit. Usually a continuous predictor of survival for which the results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference.

prob

Value between 0 and 1. If prob=0 the reference value will be the minimum of the hazard ratio curve. If prob=1 the reference value will be the maximum of the hazard ratio curve. For values between 0 and 1 the reference value will be the corresponding quantile of the variable predictor.

pred.value

Value from the variable predictor to be taken as the reference value.

conf.level

Level of confidence. Defaults to 0.95 (corresponding to 95%).

prediction.values

Vector of values ranging between minimum and maximum of the variable predictor.

round.x

Rounding of numbers in the predict.

ref.label

Label for the reference covariate. By default is the name of the covariate.

...

Other arguments.

Value

Returns a matrix with the prediction values.

Author(s)

Artur Araújo and Luís Meira-Machado

References

Carmen Cadarso-Suarez, Luis Meira-Machado, Thomas Kneib and Francisco Gude. Flexible hazard ratio curves for continuous predictors in multi-state models: a P-spline approach. Statistical Modelling, 2010, 10:291-314.

See Also

smoothHR.

Examples

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# Example 1
library(survival)
data(whas500)
fit <- coxph(Surv(lenfol, fstat)~age+hr+gender+diasbp+pspline(bmi)+pspline(los),
data=whas500, x=TRUE)
hr1 <- smoothHR(data=whas500, coxfit=fit)
predict(hr1, predictor="bmi", prob=0, conf.level=0.95)

# Example 2
hr2 <- smoothHR( data=whas500, time="lenfol", status="fstat", formula=~age+hr+gender+diasbp+
pspline(bmi)+pspline(los) )
pdval <- c(1, 2, 3, 4, 5, 6, 7, 8, 10, 12, 15, 18, 22, 25)
predict(hr2, predictor="los", pred.value=7, conf.level=0.95, prediction.values=pdval,
ref.label="Ref.")

Example output

Loading required package: survival
Loading required package: splines
      bmi       LnHR  lower .95 upper .95
 13.04546 1.26624009  0.1015945 2.4308856
 18.60004 0.79522426  0.2601568 1.3302917
 23.22377 0.32722547 -0.1670588 0.8215097
 25.94593 0.15487865 -0.3343962 0.6441535
 29.39196 0.08437564 -0.3271063 0.4958576
 36.77345 0.10139527 -0.2230930 0.4258835
 44.83886 1.63447099 -0.1676770 3.4366190
 Ref.       LnHR    lower .95  upper .95
    1 0.63958931  0.145839773 1.13333885
    2 0.41367315  0.068048050 0.75929825
    3 0.22510720 -0.003935894 0.45415030
    4 0.08661449 -0.054745149 0.22797412
    5 0.01078601 -0.061928441 0.08350045
    6 0.00000000  0.000000000 0.00000000
    7 0.03932336 -0.042984549 0.12163127
    8 0.11214610 -0.051764356 0.27605656
   10 0.29242784  0.016151585 0.56870410
   12 0.45326680  0.087874077 0.81865951
   15 0.63423058  0.142267338 1.12619381
   18 0.69915019  0.090171452 1.30812892
   22 0.46793267 -0.268804381 1.20466972
   25 0.07027577 -0.877691297 1.01824284

smoothHR documentation built on May 2, 2019, 8:20 a.m.