rr_cif: Non-parametric Cumulative Incidence Functions

Description

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(),
  ...
)

Value

For npc: matrix with columns (time, cumulative incidence). For nonparcuminc: matrix with time and cause-specific cumulative incidences.

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.

Author

Klaus K. Holst, Thomas Scheike

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.