get.score.main: Calculation of composite scores based on a main-effect model
In MMMS: Multi-Marker Molecular Signature for Treatment-specific Subgroup Identification

Description Usage Arguments Details Value Author(s) References See Also Examples

This function is to fit a main-effect model assuming no treatment-specific 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 `NULL` for not including any covariates.
`nfolds`	The number of folds for cross validation in choosing tuning parameters. The function `cv.glmnet()` in the “glmnet” package is called, which requires cross validation to choose the tuning parameter “lambda”. Default is 5.
`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 bootstrap-based p-values. A main-effect model is considered by assuming that no treatment-specific subgroups exist. This function is used for obtaining (semi)parametric bootstrap samples under the null.

A list with the following elements:

`fit`	The `glmnet` fitted object assuming no subgroups exist.
`lam.best`	The optimal `lambda` value chosen when assuming no subgroups exist.
`fit.selected`	An object returned by `coxph()` using selected biomarkers when assuming no subgroups exist.
`sfit`	An object returned by `survfit()` for bootstrap sampling.

Author: Lin Li, Tobias Guennel,Scott Marshall, Leo Wang-Kit Cheung

Contributors: Brigid M. Wilson, Dilan C. Paranagama

Maintainer: Lin Li <lli@biostatsolutions.com>

Lin Li, Tobias Guennel, Scott Marshall, Leo Wang-Kit Cheung (2014) A multi-marker molecular signature approach for treatment-specific subgroup identification with survival outcomes. The Pharmacogenomics Journal. http://dx.doi.org/10.1038/tpj.2014.9

MMMS, get.score

  # load the dataset
  data(simdat)
  attach(simdat)
  
  # get composite scores using a main-effect model
  main.only=get.score.main(time,event,treat,bio,covar,nfolds=5,alpha=0.5)