PLAC {plac} | R Documentation |
Both a conditional approach Cox model and a pairwise likelihood augmented estimator are fitted and the corresponding results are returned in a list.
PLAC(ltrc.formula, ltrc.data, id.var = "ID", td.var = NULL, td.type = "none", t.jump = NULL, init.val = NULL, max.iter = 100, print.result = TRUE, ...)
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 |
a name of the subject id in |
td.var |
a name of the time-dependent covariate in the output. |
td.type |
the type of the time-dependent covariate. Either one of
|
t.jump |
a 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 |
ltrc.formula
should have the same form as used in coxph()
; e.g., Surv(A, Y, D) ~ Z1 + Z2
.
where A
is the truncation time (tstart
), Y
is the
survival time (tstop
) and D
is the status indicator
(event
). 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.
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
Wu, F. Kim, S. and Li, Y. "A Pairwise Likelihood Augmented Estimator for Left-Truncated Data with Time-Dependent Covariates." (in preparation)
Wu, F., Kim, S., Qin, J., Saran, R. and Li, Y. (2015) "A Pairwise-Likelihood Augmented Estimator for the Cox Model Under Left-Truncation." (Submitted to Journal of American Statistical Association.) http://biostats.bepress.com/umichbiostat/paper118/
# When only time-invariant covariates are involved dat1 = sim.ltrc(n = 50)$dat PLAC(ltrc.formula = Surv(As, Ys, Ds) ~ Z1 + Z2, ltrc.data = dat1, td.type = "none") # When there is a time-dependent covariate that is independent of the truncation time dat2 = sim.ltrc(n = 50, time.dep = TRUE, distr.A = "binomial", p.A = 0.8, Cmax = 5)$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 = 50, 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")