PLAC {plac} | R Documentation |
Calculate the PLAC estimator when a time-dependent indicator presents
Description
Both a conditional approach Cox model and a pairwise likelihood augmented estimator are fitted and the corresponding results are returned in a list.
Usage
PLAC(
ltrc.formula,
ltrc.data,
id.var = "ID",
td.type = "none",
td.var = NULL,
t.jump = NULL,
init.val = NULL,
max.iter = 100,
print.result = TRUE,
...
)
Arguments
ltrc.formula |
a formula of of the form |
ltrc.data |
a data.frame of the LTRC dataset including the responses, time-invariate covariates and the jump times for the time-depnencent covariate. |
id.var |
the name of the subject id in |
td.type |
the type of the time-dependent covariate. Either one of
|
td.var |
the name of the time-dependent covariate in the output. |
t.jump |
the name of the jump time variable in |
init.val |
a list of the initial values of the coefficients and the baseline hazard function for the PLAC estimator. |
max.iter |
the maximal number of iteration for the PLAC estimator |
print.result |
logical, if a brief summary of the regression coefficient estiamtes should be printed out. |
... |
other arguments |
Details
ltrc.formula
should have the same form as used in
coxph()
; e.g., Surv(A, Y, D) ~ Z1 + Z2
. where (A, Y,
D)
are the truncation time, the survival time and the status indicator
((tstart, tstop, event)
as in coxph
).
td.type
is used to determine which C++
function will be
invoked: either PLAC_TI
(if td.type = "none"
), PLAC_TD
(if td.type = "independent"
) or PLAC_TDR
) (if td.type
%in% c("post-trunc", "pre-post-trunc")
). For td.type =
"post-trunc"
, the pre-truncation values for the time-dependent covariate
will be set to be zero for all subjects.
Value
a list of model fitting results for both conditional approach and the PLAC estimators.
Event.Time
Ordered distinct observed event times
b
Regression coefficients estiamtes
se.b
Model-based SEs of the regression coefficients estiamtes
H0
Estimated cumulative baseline hazard function
se.H0
Model-based SEs of the estimated cumulative baseline hazard function
sandwich
The sandwich estimator for (beta, lambda)
k
The number of iteration for used for the PLAC estimator
summ
A brief summary of the covariates effects
References
Wu, F., Kim, S., Qin, J., Saran, R., & Li, Y. (2018). A pairwise likelihood augmented Cox estimator for leftâtruncated data. Biometrics, 74(1), 100-108.
Examples
# When only time-invariant covariates are involved
dat1 = sim.ltrc(n = 40)$dat
PLAC(ltrc.formula = Surv(As, Ys, Ds) ~ Z1 + Z2,
ltrc.data = dat1, td.type = "none")
# When there is a time-dependent covariate that is independent of the truncation time
dat2 = sim.ltrc(n = 40, time.dep = TRUE,
distr.A = "binomial", p.A = 0.8, Cmax = 5)$dat
PLAC(ltrc.formula = Surv(As, Ys, Ds) ~ Z,
ltrc.data = dat2, td.type = "independent",
td.var = "Zv", t.jump = "zeta")
# When there is a time-dependent covariate that depends on the truncation time
dat3 = sim.ltrc(n = 40, time.dep = TRUE, Zv.depA = TRUE, Cmax = 5)$dat
PLAC(ltrc.formula = Surv(As, Ys, Ds) ~ Z,
ltrc.data = dat3, td.type = "post-trunc",
td.var = "Zv", t.jump = "zeta")