# hr.threg: Hazard ratio calculation for threshold regression model In threg: Threshold Regression

## Description

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).

## Usage

 ```1 2 3``` ```hr(object,var,timevalue,scenario) ## S3 method for class 'threg' hr(object,var,timevalue,scenario) ```

## Arguments

 `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.

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41``` ```#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)) ```

threg documentation built on May 29, 2017, 9:37 p.m.