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

Plots flexible hazard ratio curves allowing non-linear relationships between continuous predictors and survival. To better understand the effects that each continuous covariate has on the outcome, results are expressed in terms of hazard ratio curves, taking a specific covariate value as reference. Confidence bands for these curves are also derived.

1 2 3 |

`x` |
An object of class HR |

`predictor` |
Variable named in the formula or included as a predictor in the |

`prob` |
Value between 0 and 1. If |

`pred.value` |
Value from the variable |

`conf.level` |
Level of confidence. Defaults to 0.95 (corresponding to 95%). |

`round.x` |
Rounding of numbers in the plot. |

`ref.label` |
Label for the reference covariate. By default is the name of the covariate. |

`col` |
Vector of dimension 3 for the colors to plot. |

`main` |
These arguments to title have useful defaults here. |

`xlab` |
The range of x and y values with sensible defaults. |

`ylab` |
The range of x and y values with sensible defaults. |

`lty` |
Vector of dimension 2 for the line type. |

`xlim` |
The range of x and y values with sensible defaults. |

`ylim` |
The range of x and y values with sensible defaults. |

`xx` |
Vector of values (from the variable |

`...` |
Other arguments. |

No value is returned.

Artur Araújo and Luís Meira-Machado

Cadarso-Suarez, C. and Meira-Machado, L. and Kneib, T. and Gude, F. (2010). Flexible hazard ratio curves for continuous predictors in multi-state models: an application to breast cancer data. *Statistical Modelling*, **10(3)**, 291-314. doi: 10.1177/1471082X0801000303

Meira-Machado, L. and Cadarso-Suárez, C. and Gude, F. and Araújo, A. (2013). smoothHR: An R Package for Pointwise Nonparametric Estimation of Hazard Ratio Curves of Continuous Predictors, *Computational and Mathematical Methods in Medicine*, **2013**, Article ID 745742, 11 pages. doi: 10.1155/2013/745742

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
# 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)
plot(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) )
plot(hr2, predictor="los", pred.value=7, conf.level=0.95, xlim=c(0,30), round.x=1,
ref.label="Ref.", xaxt="n")
xx <- c(0, 5, 10, 15, 20, 25, 30)
axis(1, xx)
``` |

```
Loading required package: survival
Loading required package: splines
```

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