F.windows.Weibull: Dynamic prediction of death under the joint frailty-copula...

In joint.Cox: Joint Frailty-Copula Models for Tumour Progression and Death in Meta-Analysis

Description

Dynamic prediction of death using a joint frailty-copula model. Probability of death between t and t+w is calculated given a tumour progression time X and covariates Z1 and Z2. If X<=t, the prediction probability is F(t,t+w|X=x, Z1, Z2). If X>t, the prediction probability is F(t,t+w|X>t, Z1, Z2). This is a vector version of F.window.Weibull.

Usage

F.windows.Weibull(time, widths, X, Z1, Z2, beta1, beta2, eta, theta, alpha, 
                  scale1, shape1,scale2,shape2, xi1, xi3, Fplot = TRUE)

Arguments

time	prediction time (=t)
widths	length of window (=w)
X	time of tumour progression < time
Z1	a vector of covariates for progression
Z2	a vector of covariates for death
beta1	a vector of regression coefficients for progression
beta2	a vector of regression coefficients for death
eta	frailty variance
theta	copula parameter
alpha	parameter related to frailty; usually alpha=1
scale1	scale parameter related to the baseline hazard for progression
shape1	shape parameter related to the baseline hazard for progression
scale2	scale parameter related to the baseline hazard for death
shape2	shape parameter related to the baseline hazard for death
xi1	lower bound for time to event
xi3	upper bound for time to death
Fplot	if FALSE, the plot is not shown

Details

Predicted probability of death is calculated given the event status (X<=t or X>t) and covariates (Z1 and Z2).

Value

time	t
widths	w
X	X
F_event_at_X	F(t,t+w|X=x, Z1, Z2)
F_noevent	F(t,t+w|X>t, Z1, Z2)

Author(s)

Sayaka Shinohara, Takeshi Emura

References

Emura T, Nakatochi M, Matsui S, Michimae H, Rondeau V (2018), Personalized dynamic prediction of death according to tumour progression and high-dimensional genetic factors: meta-analysis with a joint model, Stat Methods Med Res 27(9):2842-58

Shinohara S, Lin YH, Michimae H, Emura T (2020), Dynamic lifetime prediction using a Weibull-based bivariate failure time model: a meta-analysis of individual-patient data, Comm Stat Simul, DOI:10.1080/03610918.2020.1855449

Examples

w=c(0,0.5,1,1.5,2)
par(mfrow=c(1,2))
F.windows.Weibull(time=1,X=0.2,widths=w,Z1=1,Z2=1,beta1=1,beta2=1,eta=0.5,theta=8,
                  alpha=1,scale1=1,shape1=1,scale2=1,shape2=1,xi1=0,xi3=3)
F.windows.Weibull(time=1,X=0.8,widths=w,Z1=1,Z2=1,beta1=1,beta2=1,eta=0.5,theta=8,
                  alpha=1,scale1=1,shape1=1,scale2=1,shape2=1,xi1=0,xi3=3)

joint.Cox documentation built on Feb. 4, 2022, 5:08 p.m.