Description Usage Arguments Details Value Author(s) References See Also Examples
This is the main function of this package. It calculates the timedependent sensitivity and specificity and area under the curve (AUC) using a nonparametric weighting adjustment. It also provides variance estimation through bootstrap.
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X 
a numeric vector of biomarker values. Same length with 
Y 
a numeric vector of time to event.Same length with 
delta 
a vector of binary indicator of event (1) or censoring (0). Same length with 
tau 
a scalar, the prediction horizon at which the prediction is evaluated. 
span 
a numeric value, the proportion of neighbour observations used in nearest neighbor method, default is 0.1. 
h 
a numeric value, the bandwidth of kernel weights, defualt is 
type 
a character value, indicating the type of kernel function used to calculate kernel weights. Default is " 
cut.off 
a vector of biomarker cutoff values at which sensitivity and specificity will be calculated.When bootstrap is requested, the corresponding confidence intervals will also be provided. 
nboot 
the number of bootstrap replications to be used for variance estimation; default is 
alpha 

n.grid 
an positive integer, the number of grid points used when calculating the ROC curve. The default is 
X.min 
the lower boundary of grid cutoff points for biomarker 
X.max 
the upper boundary of grid cutoff points for biomarker 
This function read in the prognostic biomarker value X
, the timetoevent data Y
and censoring indicator delta
to calculate
the sensitivity and specificity at the prediction horizon tau
for a series specified grid points. It uses a simple
nonparametric weight adjustments for right censored data (Li et al., 2015).
Returns a list of the following items:
ROC:
a data frame of dimension (2+n.grid) x 3
, the three columns are: grid
, sens
, and spec
.
AUC:
a data frame of one row and four columns: AUC
, standard error of AUC
, the lower and upper limits of bootstrap CI.
AUC
is calculated by integrating the area under ROC curve with trapezoidal method.
AUC2:
a data frame of one row and four columns: AUC2
, standard error of AUC2
, the lower and upper limits of bootstrap CI.
AUC2
is the AUC calculated by the concordance based formula (Li et al., 2015).
prob:
a data frame of three columns if nboot=0
: cut.off
, sens
, and spec
. If nboot>0
, another six
columns of standard error, lower and upper limits of both sens
and spec
will be added. The number of rows equals length of cut.off
.
A series of sensivitity and specificity are calculated at requested cut.off
points.
Liang Li, Cai Wu
Li, Liang, Bo Hu, and Tom Greene. "A Simple Method to Estimate the Timedependent ROC Curve Under Right Censoring." (2015). http://biostats.bepress.com/cobra/art114/
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