test.surrogate.rise {SurrogateRank} | R Documentation |
Performs RISE: Two-Stage Rank-Based Identification of High-Dimensional Surrogate Markers
Description
RISE (Rank-Based Identification of High-Dimensional Surrogate Markers) is a two-stage method to identify and evaluate high-dimensional surrogate candidates of a continuous response.
In the first stage (called screening), the high-dimensional candidates are screened one-by-one to identify strong candidates. Strength of surrogacy is assessed through a rank-based measure of the similarity in treatment effects on a candidate surrogate and the primary response. P-values corresponding to hypothesis testing on this measure are corrected for the high number of statistical tests performed.
In the second stage (called evaluation), candidates with an adjusted p-value below a given significance level are evaluated by combining them into a single synthetic marker. The surrogacy of this marker is then assessed with the univariate test as described before.
To avoid overfitting, the two stages are performed on separate data.
Usage
test.surrogate.rise(
yone,
yzero,
sone,
szero,
alpha = 0.05,
power.want.s = NULL,
epsilon = NULL,
u.y.hyp = NULL,
p.correction = "BH",
n.cores = 1,
alternative = "less",
paired = FALSE,
screen.proportion = 0.66,
return.all.screen = TRUE,
return.all.evaluate = TRUE,
return.plot.evaluate = TRUE,
evaluate.weights = TRUE
)
Arguments
yone |
numeric vector of primary response values in the treated group. |
yzero |
numeric vector of primary response values in the untreated group. |
sone |
matrix or dataframe of surrogate candidates in the treated group with
dimension |
szero |
matrix or dataframe of surrogate candidates in the untreated group with
dimension |
alpha |
significance level for determining surrogate candidates. Default is
|
power.want.s |
numeric in (0,1) - power desired for a test of treatment effect based on
the surrogate candidate. Either this or |
epsilon |
numeric in (0,1) - non-inferiority margin for determining surrogate
validity. Either this or |
u.y.hyp |
hypothesised value of the treatment effect on the primary response on the probability scale. If not given, it will be estimated based on the observations. |
p.correction |
character. Method for p-value adjustment (see |
n.cores |
numeric giving the number of cores to commit to parallel computation in
order to improve computational time through the |
alternative |
character giving the alternative hypothesis type. One of
|
paired |
logical flag giving if the data is independent or paired. If
|
screen.proportion |
numeric in (0,1) - proportion of data to be used for the screening stage.
The default is |
return.all.screen |
logical flag. If |
return.all.evaluate |
logical flag. If |
return.plot.evaluate |
logical flag. If |
evaluate.weights |
logical flag. If |
Value
a list with
-
screening.results
: a list with-
screening.metrics
: dataframe of screening results (for each candidate marker - delta, CI, sd, epsilon, p-values). -
significant_markers
: character vector of markers withp_adjusted < alpha
.
-
-
evaluate.results
: a list with-
individual.metrics
ifreturn.all.evaluate
=TRUE
, a dataframe of evaluation results for each significant marker. -
gamma.s
a list with elementsgamma.s.one
andgamma.s.zero
, giving the combined surrogate marker in the treated and untreated groups, respectively. -
gamma.s.evaluate
: a dataframe giving the evaluation ofgamma.s
-
gamma.s.plot
: a ggplot2 plot showinggamma.s
against the primary response on the rank-scale.
-
Author(s)
Arthur Hughes
Examples
# Load high-dimensional example data
data("example.data.highdim")
yone <- example.data.highdim$y1
yzero <- example.data.highdim$y0
sone <- example.data.highdim$s1
szero <- example.data.highdim$s0
rise.result <- test.surrogate.rise(yone, yzero, sone, szero, power.want.s = 0.8)