Weighted Kaplan-Meier estimator with discrete time-independent covariate
a vector of evaluation times
data frame containing the variables in formula (if is.null(formula) expected column names are: Y (time), D (status), W (strat. factor), V (left-truncation times))
list of parameters containing: alpha: fractional parameter (default=1) var: if TRUE (default) calculate variance estimate cov: if FALSE (default) do not calculate covariance matrix estimate left.limit: if TRUE calculate left-continuous estimates, else calculate right-continuous estimates rr.subset: logical vector defining subset of observations to use for response rate estimation (default: use all observations)
an object of class '"formula"' specifying the conditional survival model (only discrete covariates supported)
This function calculates the weighted Kaplan-Meier estimator for the survival function with weights based on a discrete time-independent covariate as described in Murray/Tsiatis (1996).
The survival probabilities are evaluated at each entry in the vector
times. The data frame
data must either contain the variable in
formula or, if
V (left-truncation time),
Y (censored failure time),
D (censoring indicator),
W (stratification variable).
TRUE then an estimate of the asmyptotic variance is calculated for each entry in vector
TRUE then the
n x n asymptotic
covariance matrix is estimated, where
n is the length of vector
TRUE then a left-continuous estimate of the survival function is calculated instead
of a right-continuous estimate (default). If a logical vector
rr.subset is supplied, then only a subset of observations is used to estimate the response rates.
an object of class '"wkm"'
S.~Murray and A.~A. Tsiatis. Nonparametric survival estimation using prognostic longitudinal covariates. Biometrics, 52(1):137–151, Mar. 1996.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.