TransModel: Main function for fitting the linear transformation models...

In TransModel: Fit Linear Transformation Models for Right Censored Data

Description

This function is used to fit a linear transformation model, such as the proportional hazards model or proportional odds model, to the right censored survival data.

Usage

TransModel(formula = formula(data), data = parent.frame(), r, CICB.st, subset, 
dx, iter.max, num.sim)

Arguments

formula	A survival formula based on the Surv() function, containg survival time, right censoring indicator and coavariates.
data	Data set with all the variables needed in formula.
r	Parameter in the hazard function, used to define different linear models. See details for more information.
CICB.st	Whether or not the perturbation for deriving the confidence intervals and confidence bands of survival estimates will be done. The default value is FALSE.
subset	Conditions for subsetting the dataset.
dx	Convergence tolerance. Default is 0.001.
iter.max	Maximum number of iterations before convergence. Default is 100.
num.sim	The number of perturbation, only works when CICB.st=TRUE. Default is 200.

Details

In the linear transformation model H(t)=-b'z+e, the hazard function for error term e is defined as: h(x)=exp(x)/(1+r*exp(x)), where the parameter r must be a non-negative value and can be changed for different models. For example, r=0 refers to the proportional hazards model and r=1 refers to a proportional odds model. The default value for r is 0.

Value

coefficients	Estimated coefficients for covariates in the specified linear transformation model.
vcov	Estimated covariance matix for the coefficients.
converged	Convergence status, 0 indicates converged, and number of iterations used for convergence.

References

Kani Chen, et al., Semiparametric analysis of transformation models with censored data. Biometrika, 89(3), 659-668, 2002.

Examples

beta0 = c(1,-1)
# Fit proportional hazards model
data(PH_examp)
mod1<-TransModel(formula=Surv(time,status)~gender+age,data=PH_examp,r=0)
print(mod1)
summary(mod1)
mod1$coefficients
mod1$vcov
mod1$converged

# Fit proportional odds model
data(PO_examp)
mod2=TransModel(Surv(time,status)~gender+age,data=PO_examp,r=1)
print(mod2)
summary(mod2)

Example output

Loading required package: survival
Loading required package: MASS
Call:
TransModel.default(formula = Surv(time, status) ~ gender + age, 
    data = PH_examp, r = 0)

Coefficients:
    gender        age 
 0.9610871 -1.0393602 

Covariance Matrix:
             gender          age
gender  0.010456412 -0.001755734
age    -0.001755734  0.003681565
Call:
TransModel.default(formula = Surv(time, status) ~ gender + age, 
    data = PH_examp, r = 0)

        Estimate    StdErr  z.value   p.value    
gender  0.961087  0.102257   9.3988 < 2.2e-16 ***
age    -1.039360  0.060676 -17.1297 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
    gender        age 
 0.9610871 -1.0393602 
             gender          age
gender  0.010456412 -0.001755734
age    -0.001755734  0.003681565
[1] 0 4
Call:
TransModel.default(formula = Surv(time, status) ~ gender + age, 
    data = PO_examp, r = 1)

Coefficients:
   gender       age 
-1.015012  1.093923 

Covariance Matrix:
             gender          age
gender  0.034101831 -0.001399366
age    -0.001399366  0.009024690
Call:
TransModel.default(formula = Surv(time, status) ~ gender + age, 
    data = PO_examp, r = 1)

        Estimate    StdErr z.value   p.value    
gender -1.015012  0.184667 -5.4965 3.875e-08 ***
age     1.093923  0.094998 11.5152 < 2.2e-16 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

TransModel documentation built on Feb. 3, 2022, 5:08 p.m.