findcutpoints {CutpointsOEHR} | R Documentation |

Use optimal equal-HR method to determine two optimal cutpoints of a continuous predictor that has a U-shape relationship with survival outcomes based on Cox regression model.

findcutpoints(cox_pspline_fit, data, nquantile = 100, exclude = 0.05, eps = 0.01, shape = "U")

`cox_pspline_fit` |
Cox model with psplined x, e.g. coxph(Surv(t,d)~pspline(x,df=0,caic=T),data=test). |

`data` |
a dataframe contain survival outcome and a continuous variable which needs to find two optimal cutpoints. |

`nquantile` |
an integer; the default value is 100, which means using the 100-quantiles of log relative hazard to find cutpoints. |

`exclude` |
a decimals; it is used for excluding extreme values of log relative hazardthe. The default value is 0.05, which log relative hazard values smaller than 5th percentile or larger than 95th percentile are excluded. |

`eps` |
a decimals; the default value is 0,01. It restrict the difference between the log relative hazard values of two cadidate cutpoints to be less than 0.01. |

`shape` |
a string; equals "U" or "inverseU" |

A function to find two optimal cutpoints

#### Example 1. Find two optimal cutpoints in an univariate Cox model # Fit an univariate Cox model with pspline require(survival) result <- coxph(Surv(t,d)~pspline(x,df=0,caic=TRUE),data=test) # Visualize the relationship # Explore whether there is a U-shaped relationship between x and log relative hazard termplot(result,se=TRUE,col.term=1,ylab='log relative hazard') # Find two opitmal cutpoints using optimal equal-HR method. cuts <- findcutpoints(cox_pspline_fit = result, data = test, shape='U') cuts$optimal # output two optimal cutpoints #### Example 2. Find two optimal cutpoints in a multivariate Cox model # Fit a multivariate Cox model with pspline # The independent variable which is need to find cutpoints should be placed before other covariates. # To find cutpoints of x, Surv(t,d)~pspline(x)+x1 should be used instead of Surv(t,d)~x1+pspline(x) require(survival) result <- coxph(Surv(t,d)~pspline(x,df=0,caic=TRUE)+x1,data=test) # The rest procedure is the same as example 1 # Visualize the relationship # Explore whether there is a U-shaped relationship between x and log relative hazard termplot(result,se=TRUE,col.term=1,ylab='log relative hazard') # Find two opitmal cutpoints of the first independent variable. cuts <- findcutpoints(cox_pspline_fit = result, data = test, shape='U') cuts$optimal # output two optimal cutpoints

[Package *CutpointsOEHR* version 0.1.2 Index]