Bone Marrow Transplantation Data

This function can be used to estimate hazard ratios at selected time points for specified scenarios (based on given categories or value settings of covariates).

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`object` |
a threg object. |

`var` |
specifies the categorical variable for the calculation of hazard ratios. Such categorical variable must be a factor variable that has been used in threg() that returns the threg object. |

`timevalue` |
specifies a value of time at which the hazard ratios are calculated. A vector is allowed. |

`scenario` |
specifies a scenario where the hazard ratios are calculated. |

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#load the data "lkr"
data("lkr")
#Transform the "treatment2" variable into factor variable "f.treatment2" .
lkr$f.treatment2=factor(lkr$treatment2)
#fit the threshold regression model on the factor variable "f.treatment2",
fit<-threg(Surv(weeks, relapse)~ f.treatment2|f.treatment2,data = lkr)
fit
#calculate the hazard ratio of the drug B group v.s. the standard group at
#week 5 (this hazard ratio is calculated as 2.08)
hr.threg(fit,var=f.treatment2,timevalue=5)
#calculate the hazard ratio of the drug B group v.s. the standard group at
#week 20 (this hazard ratio is calculated as 0.12)
hr.threg(fit,var=f.treatment2,timevalue=20)
#As a comparison, fit the Cox proportion hazards model on "f.treatment2",
#and the Cox model gives a constant hazard ratio, 0.73.
summary(coxph(Surv(weeks, relapse) ~ f.treatment2, data = lkr))
#load the data "bmt"
data("bmt")
#Transform the "group" and "fab" variables into factor variables
#"f.group" and "f.fab".
bmt$f.group=factor(bmt$group)
bmt$f.fab=factor(bmt$fab)
#fit a threshold regression model on the "bmt" dataset, by using "recipient_age" and
#"f.fab" as the predictors for ln(y0), and "f.group" and "f.fab" as predictors for mu.
fit<-threg(Surv(time, indicator)~ recipient_age+f.fab|f.group+f.fab, data = bmt)
fit
#Calculate the hazard ratio for
#"f.group" for the specified scenario that "the patient age is 18 years old and
#the FAB classification is 0", at the time ``500 days''.
hr.threg(fit,var=f.group,timevalue=500,scenario=recipient_age(18)+f.fab1(0))
``` |

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