dscoreMSM (version 0.1.0)

cphGM: 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
)

Value

Estimates obtained from coxph model with the frailty terms.

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

Author

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra K. Vishwakarma

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.

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

# \donttest{
##
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
##
# }