joint.Cox (version 3.16)

jointCox.Weibull.reg: Weibull-based Likelihood Estimation under the Joint Cox Models Between Tumour Progression and Death for Meta-Analysis

Description

Perform Weibull regression analyses under a copula-based joint Cox proportional hazards model between tumour progression and death for meta-analysis, which is proposed by Wu et al. (2020).

Usage

jointCox.Weibull.reg(t.event, event, t.death, death, Z1, Z2, group, alpha = 1,
 Randomize_num = 10, u.min = 0.001, u.max = 10, Adj = 500,convergence.par=FALSE)

Arguments

t.event

a vector for time-to-tumour progression (TTP)

event

a vector for progression indicator (=1 if progression; =0 if not progression)

t.death

a vector for overall survival (OS), i.e., time-to-death

death

a vector for death indicator(=1 if death; =0 if not death)

Z1

a matrix for covariates associated with TTP; ncol(Z1)=the number of covariates

Z2

a matrix for covariates associated with OS; ncol(Z2)=the number of covariates

group

a vector for group identification numbers, like 1,2,3....

alpha

A value related to the frailty (e.g., alpha=0 or =1); alpha=1 is default

Randomize_num

The number of randomizations for the ititial p0

u.min

the lower bound of the numerical integration for the frailty term

u.max

the upper bound of the numerical integration for the frailty term

Adj

Numerical adjustment to prevent overflow; Adj=500 is recommended

convergence.par

If TRUE, the converged estimate, gradient, and Hessian matrix are given (log-transformed)

Value

count

Count for event occurences

beta1

Regression coefficient for Z1

beta2

Regression coefficient for Z2

eta

Frailty parameter (variance)

theta

Copula parameter under the Clayton copula

tau

Kendall's tau corresponding to the copula parameter

scale1

Scale parameter for the Weibull model of TTP

shape1

Shape parameter for the Weibull model of TTP

scale2

Scale parameter for the Weibull model of OS

shape2

Shape parameter for the Weibull model of OS

convergence

convergence results for maximizing penalized likelihood

convergence.parameters

converged estimate, gradient, and Hessian matrix (log-transformed)

Error

"Error in integrate(func1, 0.001, 10, stop.on.error = FALSE):non-finite function value", an error occurring when the penalized likelihood is maximizad by "nlm". The error may frequently occur during the iterations for maximizing the penalized likelihood, but is not crucial (can simply be ignored).

Warning

"NA/Inf replaced by maximum positive value", an error occurring when the penalized likelihood is maximizad by "nlm". The error frequently occurs during the iterations for maximizing the penalized likelihood, but is not crucial (can simply be ignored).

Details

We employ "nlm" routine to maximize the penalized likelihood function with the initial value described in Wu et al. (2020). If "nlm" does not converge, then we randomize the initial value by adding uniform random variables (Hu and Emura, 2015).

References

Wu BH, Michimae H, Emura T (2020), Meta-analysis of individual patient data with semi-competing risks under the Weibull joint frailty-copula model. Comp Stat 35(4):1525-52

Emura T, Nakatochi M, Murotani K, Rondeau V (2017), A joint frailty-copula model between tumour progression and death for meta-analysis, Stat Methods Med Res 26(6): 2649-66

Hu YH, Emura T (2015), Maximum likelihood estimation for a special exponential family under random double-truncation, Comp Stat 30 (4): 1199-1229

Examples

# NOT RUN {
data(dataOvarian)
t.event=dataOvarian$t.event
event=dataOvarian$event
t.death=dataOvarian$t.death
death=dataOvarian$death
Z1=dataOvarian$CXCL12
group=dataOvarian$group
alpha_given=0

#set.seed(1)
#jointCox.Weibull.reg(t.event=t.event,event=event,t.death=t.death,death=death,
#                 Z1=Z1,Z2=Z1,group=group,alpha=alpha_given,Adj=500)
# }