Functions for computing and visualizing non-parametric cumulative incidence estimates, as well as dependence measures (odds ratio, relative risk) for bivariate competing risks data.
Usage
rr_cif(
cif,
data,
cause = NULL,
cif2 = NULL,
times = NULL,
cause1 = 1,
cause2 = 1,
cens.code = NULL,
cens.model = "KM",
Nit = 40,
detail = 0,
clusters = NULL,
theta = NULL,
theta.des = NULL,
step = 1,
sym = 0,
weights = NULL,
same.cens = FALSE,
censoring.weights = NULL,
silent = 1,
par.func = NULL,
dpar.func = NULL,
dimpar = NULL,
score.method = "nlminb",
entry = NULL,
estimator = 1,
trunkp = 1,
admin.cens = NULL,
...
)
or_cif(
cif,
data,
cause = NULL,
cif2 = NULL,
times = NULL,
cause1 = 1,
cause2 = 1,
cens.code = NULL,
cens.model = "KM",
Nit = 40,
detail = 0,
clusters = NULL,
theta = NULL,
theta.des = NULL,
step = 1,
sym = 0,
weights = NULL,
same.cens = FALSE,
censoring.weights = NULL,
silent = 1,
par.func = NULL,
dpar.func = NULL,
dimpar = NULL,
score.method = "nlminb",
entry = NULL,
estimator = 1,
trunkp = 1,
admin.cens = NULL,
...
)
random.cif(cif, ...)
Grandom.cif(cif, ...)
predictPairPlack(cif1, cif2, status1, status2, theta)
npc(T, cause, same.cens = TRUE, sep = FALSE)
nonparcuminc(t, status, cens = 0)
plotcr(
x,
col,
lty,
legend = TRUE,
which = 1:2,
cause = 1:2,
ask = prod(par("mfcol")) < length(which) && dev.interactive(),
...
)Arguments
- cif
a cumulative incidence model object (from timereg).
- data
a data.frame with the variables.
- cause
causes to plot.
- cif2
optional second CIF model if different from first.
- times
time points for evaluation.
- cause1
cause for first coordinate.
- cause2
cause for second coordinate.
- cens.code
censoring code value.
- cens.model
censoring model type (default
"KM").- Nit
maximum number of iterations.
- detail
level of output detail.
- clusters
cluster variable name or vector.
- theta
dependence parameter(s).
- theta.des
design matrix for theta.
- step
step size for optimization.
- sym
if 1, symmetric dependence structure.
- weights
optional weights.
- same.cens
logical; if TRUE, uses joint censoring weights.
- censoring.weights
optional pre-computed censoring weights.
- silent
verbosity level.
- par.func
optional parameter function.
- dpar.func
optional derivative of parameter function.
- dimpar
dimension of parameter vector.
- score.method
optimization method (default
"nlminb").- entry
optional entry time variable.
- estimator
estimator type.
- trunkp
truncation probability.
- admin.cens
administrative censoring time.
- ...
additional arguments.
- cif1
CIF values for subject 1 (for
predictPairPlack).- status1
status for subject 1.
- status2
status for subject 2.
- T
matrix with columns: time1, time2, status1, status2 (for
npc).- sep
logical; if TRUE, uses separate censoring models for each subject.
- t
vector of event/censoring times (for
nonparcuminc).- status
vector of status codes (for
nonparcuminc).- cens
censoring code (default 0).
- x
data matrix or competing risks object.
- col
colors for curves.
- lty
line types for curves.
- legend
logical; if TRUE, add legend.
- which
which plots to show.
- ask
logical; if TRUE, prompt before new page.
Value
For npc: matrix with columns (time, cumulative incidence).
For nonparcuminc: matrix with time and cause-specific cumulative incidences.
Details
npc computes bivariate non-parametric cumulative incidence using
inverse-probability-of-censoring weights.
nonparcuminc computes univariate non-parametric cumulative incidence
for multiple causes.
plotcr plots cumulative incidence curves for competing risks using
the prodlim package.
or_cif fits an odds-ratio model for bivariate cumulative incidence.
rr_cif fits a relative-risk model for bivariate cumulative incidence.
random.cif and Grandom.cif are aliases for random_cif
and Grandom_cif (random effects CIF models).
predictPairPlack predicts pairwise joint probabilities under a
Plackett (odds-ratio) dependence model.
matplot.mets.twostage produces matrix-plots of concordance over time
from a twostage object.