This function is to fit a maineffect model assuming no treatmentspecific subgroups exist (under the null).
1 2  get.score.main(time, event, treat, bio, covar = NULL, nfolds = 5,
alpha = 0.5)

time 
A numeric vector containing the follow up time for right censored data. 
event 
A numeric vector containing the status indicator, normally 0=alive, 1=dead. 
treat 
A numeric vector containing the treatment indicator: 1=treatment of interest, 0=alternative treatment (e.g. placebo or standard of care). 
bio 
A numeric data frame or matrix containing biomarker values. 
covar 
A numeric matrix containing clinical covariates. Default is 
nfolds 
The number of folds for cross validation in choosing tuning parameters. The function 
alpha 
A scalar for the elasticnet mixing parameter as in the “glmnet” package (0=ridge, 1=lasso). A fixed value is supposed to be used, without searching for the optimal alpha value. Default is 0.5. 
This function is a function called by MMMS()
to obtain bootstrapbased pvalues. A maineffect model is considered by assuming that no treatmentspecific subgroups exist. This function is used for obtaining (semi)parametric bootstrap samples under the null.
A list with the following elements:
fit 
The 
lam.best 
The optimal 
fit.selected 
An object returned by 
sfit 
An object returned by 
Author: Lin Li, Tobias Guennel,Scott Marshall, Leo WangKit Cheung
Contributors: Brigid M. Wilson, Dilan C. Paranagama
Maintainer: Lin Li <lli@biostatsolutions.com>
Lin Li, Tobias Guennel, Scott Marshall, Leo WangKit Cheung (2014) A multimarker molecular signature approach for treatmentspecific subgroup identification with survival outcomes. The Pharmacogenomics Journal. http://dx.doi.org/10.1038/tpj.2014.9
MMMS
, get.score
1 2 3 4 5 6 
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