A set of high-dimensional surrogate candidates are evaluated jointly. Strength of surrogacy is assessed through a rank-based measure of the similarity in treatment effects on a candidate surrogate and the primary response.
rise.evaluate(
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,
return.all.evaluate = TRUE,
return.plot.evaluate = TRUE,
evaluate.weights = TRUE,
screening.weights = NULL,
markers = NULL
)A list with:
individual.metrics If return.all.evaluate = TRUE, a dataframe of
evaluation results for each significant marker.
gamma.s A list with elements gamma.s.one and gamma.s.zero, giving
the combined surrogate marker in the treated and untreated groups, respectively.
gamma.s.evaluate A dataframe giving the evaluation of gamma.s.
gamma.s.plot A ggplot2 plot showing gamma.s against the primary response
on the rank-scale.
numeric vector of primary response values in the treated group.
numeric vector of primary response values in the untreated group.
matrix or dataframe of surrogate candidates in the treated group
with dimension n1 x p where n1 is the number of treated samples
and p the number of candidates. Sample ordering must match exactly
yone.
matrix or dataframe of surrogate candidates in the untreated group
with dimension n0 x p where n0 is the number of untreated samples
and p the number of candidates. Sample ordering must match exactly
yzero.
significance level for determining surrogate candidates. Default is
0.05.
numeric in (0,1) - power desired for a test of treatment effect based
on the surrogate candidate. Either this or epsilon argument must
be specified.
numeric in (0,1) - non-inferiority margin for determining surrogate
validity. Either this or power.want.s argument must be specified.
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.
character. Method for p-value adjustment (see p.adjust()
function). Defaults to the Benjamini-Hochberg method ("BH").
numeric giving the number of cores to commit to parallel computation
in order to improve computational time through the pbmcapply()
function. Defaults to 1.
character giving the alternative hypothesis type. One of
c("less","two.sided"), where "less" corresponds to a
non-inferiority test and "two.sided" corresponds to a two one-sided test
procedure. Default is "less".
logical flag giving if the data is independent or paired. If
FALSE (default), samples are assumed independent. If TRUE,
samples are assumed to be from a paired design. The pairs are specified
by matching the rows of yone and sone to the rows of
yzero and szero.
logical flag. If TRUE (default), a dataframe will be returned
giving the evaluation of each individual marker passed to the
evaluation stage.
logical flag. If TRUE (default), a ggplot2 object will be
returned allowing the user to visualise the association between the
composite surrogate on the individual-scale.
logical flag. If TRUE (default), the composite surrogate is
constructed with weights such that surrogates which are
predicted to be stronger receive more weight.
dataframe with columns marker and weight giving the weight
in for the evaluation. Typically this is taken directly from the
screening stage as the output from the rise.screen() function.
Must be given if evaluate.weights is TRUE.
a vector of marker names (column names of szero and sone) to evaluate. If not given, will default to evaluating all markers in the dataframes.
Arthur Hughes
# 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
# \donttest{
rise.evaluate.result <- rise.evaluate(yone, yzero, sone, szero, power.want.s = 0.8)# }
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