filter {SubgrpID} | R Documentation |
filter
Description
Filter function for Prognostic and preditive biomarker signature development for Exploratory Subgroup Identification in Randomized Clinical Trials
Usage
filter(
data,
type = "c",
yvar,
xvars,
censorvar = NULL,
trtvar = NULL,
trtref = 1,
n.boot = 50,
cv.iter = 20,
pre.filter = length(xvars),
filter.method = NULL
)
Arguments
data |
input data frame |
type |
type of response variable: "c" continuous; "s" survival; "b" binary |
yvar |
variable (column) name for response variable |
xvars |
vector of variable names for predictors (covariates) |
censorvar |
variable name for censoring (1: event; 0: censor), default = NULL |
trtvar |
variable name for treatment variable, default = NULL (prognostic signature) |
trtref |
coding (in the column of trtvar) for treatment arm, default = 1 (no use for prognostic signature) |
n.boot |
number of bootstrap for the BATTing procedure |
cv.iter |
Algotithm terminates after cv.iter successful iterations of cross-validation, or after max.iter total iterations, whichever occurs first |
pre.filter |
NULL (default), no prefiltering conducted;"opt", optimized number of predictors selected; An integer: min(opt, integer) of predictors selected |
filter.method |
NULL (default), no prefiltering; "univariate", univaraite filtering; "glmnet", glmnet filtering |
Details
Filter function for predictive/prognostic biomarker candidates for signature development
The function contains two algorithms for filtering high-dimentional multivariate (prognostic/predictive) biomarker candidates via univariate fitering (used p-values of group difference for prognostic case, p-values of interaction term for predictive case); LASSO/Elastic Net method. (Tian L. et al 2012)
Value
var |
a vector of filter results of variable names |
References
Tian L, Alizadeh A, Gentles A, Tibshirani R (2012) A Simple Method for Detecting Interactions between a Treatment and a Large Number of Covariates. J Am Stat Assoc. 2014 Oct; 109(508): 1517-1532.
Examples
# no run