Description Usage Arguments Details Value Author(s) References Examples
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).
1 2 | F.window.Weibull(time, width, X, Z1, Z2, beta1, beta2, eta, theta, alpha,
scale1, shape1,scale2,shape2, xi1, xi3, Fplot = TRUE)
|
time |
prediction time (=t) |
width |
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 |
Predicted probability of death is calculated given the event status (X<=t or X>t) and covariates (Z1 and Z2).
time |
t |
width |
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) |
Sayaka Shinohara, Takeshi Emura
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
1 2 3 4 5 6 | w=1
par(mfrow=c(1,2))
F.window.Weibull(time=1,X=0.2,width=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.window.Weibull(time=1,X=0.8,width=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)
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