Description Usage Arguments Details Value Author(s) References See Also Examples
This is the main function of this package. It calculates the time-dependent 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 cut-off 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 cut-off points for biomarker |
X.max |
the upper boundary of grid cut-off points for biomarker |
This function read in the prognostic biomarker value X
, the time-to-event 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 Time-dependent ROC Curve Under Right Censoring." (2015). http://biostats.bepress.com/cobra/art114/
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