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). This function is a simpler version of F.windows. The guide for using this function shall be explained by Emura et al. (2019).
1 2 | F.prediction(time, widths, X, Z1, Z2, beta1, beta2, eta, theta, alpha,
g, h, xi1, xi3, Fplot = TRUE)
|
time |
prediction time (=t) |
widths |
length of window (=w) |
X |
time of tumour progression; if tumour progression does not occur before time t, one can set an arbitrary value X greater than t |
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 |
g |
parameters related to the baseline hazard for progression |
h |
parameters 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 |
widths |
w |
X |
X |
F |
F(t,t+w|X=x, Z1, Z2) or F(t,t+w|X>t, Z1, Z2) |
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
Emura T, Michimae H, Matsui S (2019-), A clinician's guide for dynamic risk prediction of death using an R package joint.Cox, submitted for publication.
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