epa | R Documentation |

An Epanechnikov kernel function based smoother for smoothing the baseline excess hazard calculated by the `rsadd`

function with the `EM`

method.

epa(fit,bwin,times,n.bwin=16,left=FALSE)

`fit` |
Fit from the additive relative survival model using the |

`bwin` |
The relative width of the smoothing window (default is 1). |

`times` |
The times at which the smoother is to be evaluated. If missing, it is evaluated at all event times. |

`n.bwin` |
Number of times that the window width may change. |

`left` |
If |

The function performs Epanechnikov kernel smoothing. The follow up time is divided (according to percentiles of event times) into several intervals (number of intervals defined by `n.bwin`

) in which the width is calculated as a factor of the maximum span between event times.
Boundary effects are also taken into account on both sides.

A list with two components:

`lambda` |
the smoothed excess baseline hazard function |

`times` |
the times at which the smoothed excess baseline hazard is evaluated. |

Package. Pohar M., Stare J. (2006) "Relative survival analysis in R." Computer Methods and Programs in Biomedicine, **81**: 272–278

Relative survival: Pohar, M., Stare, J. (2007) "Making relative survival analysis relatively easy."
Computers in biology and medicine, **37**: 1741–1749.

EM algorithm: Pohar Perme M., Henderson R., Stare, J. (2009) "An approach to estimation in relative survival regression." Biostatistics, **10**: 136–146.

`rsadd`

,

data(slopop) data(rdata) #fit an additive model with the EM method fit <- rsadd(Surv(time,cens)~sex+age,rmap=list(age=age*365.241), ratetable=slopop,data=rdata,int=5,method="EM") sm <- epa(fit) plot(sm$times,sm$lambda)

relsurv documentation built on Dec. 28, 2022, 2:25 a.m.

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