BMsel {biospear}  R Documentation 
This function enables to fit a Cox regression model for a prognostic or a biomarkerbytreatment interaction setting subject to a selection procedure to perform variable selection.
BMsel(data, x, y, z, tt, inter, std.x = TRUE, std.i = FALSE, std.tt = TRUE, method = c('alassoL', 'alassoR', 'alassoU', 'enet', 'gboost', 'glasso', 'lasso', 'lasso1se', 'lassoAIC', 'lassoBIC', 'lassoHQIC', 'lassopct', 'lassopcvl','lassoRIC', 'modCov', 'PCAlasso', 'PLSlasso', 'ridge', 'ridgelasso', 'stabSel', 'uniFDR'), folds = 5, uni.fdr = 0.05, uni.test = 1, ss.rando = F, ss.nsub = 100, ss.fsub = 0.5, ss.fwer = 1, ss.thr = 0.6, dfmax = ncol(data) + 1, pct.rep = 1, pct.qtl = 0.95, showWarn = TRUE, trace = TRUE) ## S3 method for class 'resBMsel' summary(object, show = TRUE, keep = c('tt', 'z', 'x', 'xt'), add.ridge = FALSE, ...)
data 
input 
x 
colnames or position of the biomarkers in 
y 
colnames or position of the survival outcome in 
z 
colnames or position of the clinical covariates in 
tt 
colname or position of the treatment in 
inter 
logical parameter indicating if biomarkerbytreatment interactions should be computed. 
std.x 
logical parameter indicating if the biomarkers should be standardized (i.e. substracting by the mean and dividing by the standard deviation of each biomarker). 
std.i 
logical parameter indicating if the biomarkerbytreatment interactions should be standardized (i.e. substracting by the mean and dividing by the standard deviation of each interaction). 
std.tt 
logical parameter indicating if the treatment should be recoded as +/0.5. 
method 
methods computed to perform variable selection and to estimate the regression coefficients. See the Details section to understand all the implemented methods. 
folds 
number of folds. 
uni.fdr, uni.test 
specific parameters for the univariate procedure. 
ss.fsub, ss.fwer, ss.nsub, ss.rando, ss.thr 
specific parameters for the stability selection. 
dfmax 
limit the maximum number of variables in the model. Useful for very large number of covariates to limit the time computation. 
pct.rep, pct.qtl 
specific parameters for the percentile lasso.

showWarn 
logical parameter indicating if warnings should be printed. 
trace 
logical parameter indicating if messages should be printed. 
object 
object of class ' 
show 
parameter for the 
keep 
parameter for the 
add.ridge 
parameter for the 
... 
The objects x
, y
, z
(if any) and tt
(if any) are mandatory for nonsimulated data sets.
The method
parameter specifies the approaches for model selection. Most of these selection methods are based on the lasso penalty (Tibshirani, 1996). The tuning parameter is usually chosen though the crossvalidated loglikelihood criterion (cvl), except for the empirical extensions of the lasso
in which additional penalties to the cvl (given with a suffix, e.g. lassopcvl
) are used to estimate the tuning parameter. Other methods based on the lasso are also implemented such as the adaptive lasso (alassoL
, alassoR
and alassoU
for which the last letter indicates the procedure used to estimate the preliminary weights: "L
" for lasso, "R
" for ridge and "U
" for univariate), the elasticnet (enet
) or the stability selection (stabSel
). For the interaction setting, specific methods were implemented: to reduce/control the main effects matrix (i.e. ridge (ridgelasso
) or dimension reduction (PCAlasso
or PLSlasso
)), to select or discard main effects and interactions simultaneously (i.e. grouplasso (glasso
)), or to include only the interaction part in the model (i.e. modCov
). Some selection methods not based on penalized regression are also proposed: univariate selection (uniFDR
), gradient boosting (gboost
). The ridge
penalty without selection can also be applied.
For all methods but the uniFDR
, tuning parameters are chosen by maximizing the crossvalidated loglikelihood (maxcvl). For the elasticnet, the "alpha" parameter (tradeoff between ridge and lasso) is investigated among a predefined grid of values (as suggested by the authors, Zou et al. 2005) and the "lambda" is estimated by maximizing the abovementioned cvl criterion for each of the "alpha" parameter. The combination (alpha; lambda) that maximizes the cvl is finally retained. For the gradient boosting, the number of steps is also estimated by the maxcvl. For the univariate selection, the tuning parameter is the FDR threshold defined by the user to control for multiple testing (using the parameter uni.fdr
).
We have included the possibility to adjust for clinical covariates (z
) for all methods. For penalized regressions, these covariates are considered as unpenalized. For the gradient boosting, a model with clinical covariates is preliminary implemented and regression coefficients are fixed as offset in the boosting approach. For the univariate selection, clinical covariates are forced as adjustment variables in the model and the FDR is calculated on the Wald pvalues of the coefficient associated with the biomarker in such models.
An object of class 'resBMsel
' containing the list of the selected biomarkers and their estimated regression coefficients for the chosen methods.
Nils Ternes, Federico Rotolo, and Stefan Michiels
Maintainer: Nils Ternes nils.ternes@yahoo.com
Ternes N, Rotolo F and Michiels S.
Empirical extensions of the lasso penalty to reduce
the false discovery rate in highdimensional Cox regression models.
Statistics in Medicine 2016;35(15):25612573.
doi:10.1002/sim.6927
Ternes N, Rotolo F, Heinze G and Michiels S.
Identification of biomarkerbytreatment interactions in randomized
clinical trials with survival outcomes and highdimensional spaces.
Biometrical journal. In press.
doi:10.1002/bimj.201500234
Tibshirani R.
Regression shrinkage and selection via the lasso.
Journal of the Royal Statistical Society, Ser B 1996;58:267288.
######################################## # Simulated data set ######################################## ## Low calculation time set.seed(654321) sdata < simdata( n = 500, p = 20, q.main = 3, q.inter = 0, prob.tt = 0.5, alpha.tt = 0, beta.main = 0.8, b.corr = 0.6, b.corr.by = 4, m0 = 5, wei.shape = 1, recr = 4, fu = 2, timefactor = 1) resBM < BMsel( data = sdata, method = c("lasso", "lassopcvl"), inter = FALSE, folds = 5) summary(resBM) ## Not run: ## Moderate calculation time set.seed(123456) sdata < simdata( n = 500, p = 100, q.main = 5, q.inter = 5, prob.tt = 0.5, alpha.tt = 0.5, beta.main = c(0.5, 0.2), beta.inter = c(0.7, 0.4), b.corr = 0.6, b.corr.by = 10, m0 = 5, wei.shape = 1, recr = 4, fu = 2, timefactor = 1, active.inter = c("bm003", "bm021", "bm044", "bm049", "bm097")) resBM < BMsel( data = sdata, method = c("lasso", "lassopcvl"), inter = TRUE, folds = 5) summary(resBM) summary(resBM, keep = "xt") ## End(Not run) ######################################## # Breast cancer data set ######################################## ## Not run: data(Breast) dim(Breast) set.seed(123456) resBM < BMsel( data = Breast, x = 4:ncol(Breast), y = 2:1, tt = 3, inter = FALSE, std.x = TRUE, folds = 5, method = c("lasso", "lassopcvl")) summary(resBM) ## End(Not run) ######################################## ########################################