get.score: Calculation of composite scores based on an interaction 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 calculate composite scores of a multi-marker molecular signature based on an interaction model.

1 2	get.score(time, event, treat, bio, covar = NULL, nfolds = 5, alpha = 0.5, pos.direction = FALSE)

`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.
`pos.direction`	A logical value indicating whether a subgroup with hazard ratio > 1 is desirable. Default is `FALSE`, i.e. a hazard ratio < 1 is desirable.

This function is a function called by MMMS() to calculate MMMS composite scores. An interaction model is considered by assuming that a treatment-specific subgroup exists. The composite scores based on interaction terms and main-effect terms are both calculated via elastic net as implemented by the “glmnet” package. The composite scores based on interaction terms are used for identifying treatment-specific subgroups, while those based on main-effect terms are used for adjusting for biomarker main effect.

A list with the following elements:

`score`	The composite scores based interaction terms for the treatment arm of interest (`treat==1`).
`score.all`	The composite scores based on interaction terms for all patients.
`score.main`	The composite scores based on main-effect terms.
`coefs`	Elnet coefficient estimates for interaction terms.
`coefs.main`	Elnet coefficient estimates for main-effect terms.
`fit`	The `glmnet` fitted object for obtaining the MMMS composite scores.
`lam.best`	The optimal `lambda` value chosen for obtaining the MMMS composite scores.
`treat`	The treatment variable in the input data.
`alpha`	The `alpha` value used for obtaining the MMMS composite scores.

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

  # load the dataset
  data(simdat)
  attach(simdat)
  
  # get composite scores using an interaction model
  score = get.score(time,event,treat,bio,covar,nfolds=5,alpha=0.5,
                    pos.direction=FALSE)