cphGM: CoxPH model with parametric baseline and frailty terms

In dscoreMSM: Survival Proximity Score Matching in Multi-State Survival Model

CoxPH model with parametric baseline and frailty terms

Description

Function for estimating the parameters of coxPH model with frailty terms

Usage

cphGM(
  formula,
  fterm,
  Time,
  status,
  id,
  data,
  bhdist,
  method = "L-BFGS-B",
  maxit = 200
)

Arguments

formula	survival model formula like Surv(time,status)~x1+x2
fterm	frailty term like c('gamma','center'). Currently we have the option for gamma distribution.
Time	survival time column
status	survival status column
id	id column
data	dataset
bhdist	distribution of survival time at baseline. Available option 'weibull','exponential','gompertz',
method	options are 'LFGS','L-BFGS-G','CG' etc. for more details see optim
maxit	maximum number of iteration

Details

The hazard model is as follows:

h_i(t)=z_ih_0(t)exp(\textbf{x}_i\beta)\;;i=1,2,3,...,n

where baseline survival distribution could be Weibull distribution and the hazard function is:

h_0(t)=\rho \lambda t^{\rho-1}

. Similarly we can have Expoenetial, log logistic distribution. The following are the formula for hazard and cummulative hazard function For exponential: h_0(t)=\lambda and H_0(t)=\lambda t\;\lambda>0 Gompertz: h_0(t)=\lambda exp(\gamma t) and H_0(t)=\frac{\lambda}{\gamma}(exp(\gamma t)-1);\lambda,\gamma>0 The frailty term z_i follows Gamma distribution with parameter \theta. The parameter estimates are obtained by maximising the log likelihood

\prod_{i=1}^{n}l_i(\beta,\theta,\lambda,\rho)

The method argument allows the user to select suitable optimisation method available in optim function.

Value

Estimates obtained from coxph model with the frailty terms.

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra K. Vishwakarma

References

Vishwakarma, G. K., Bhattacherjee, A., Rajbongshi, B. K., & Tripathy, A. (2024). Censored imputation of time to event outcome through survival proximity score method. Journal of Computational and Applied Mathematics, 116103;

Bhattacharjee, A., Vishwakarma, G. K., Tripathy, A., & Rajbongshi, B. K. (2024). Competing risk multistate censored data modeling by propensity score matching method. Scientific Reports, 14(1), 4368.

See Also

dscore,simfdata

Examples

##
X1<-matrix(rnorm(1000*2),1000,2)
simulated_data<-simfdata(n=1000,beta=c(0.5,0.5),fvar=0.5,
X=X1)
model1<-cphGM(formula=Surv(time,status)~X1+X2,
fterm<-c('gamma','id'),Time="time",status="status",
id="id",data=simulated_data,bhdist='weibull')
model1
##

dscoreMSM documentation built on April 4, 2025, 12:38 a.m.