MMMS (version 0.1)

get.score: Calculation of composite scores based on an interaction model

Description

This function is to calculate composite scores of a multi-marker molecular signature based on an interaction model.

Usage

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

Arguments

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.

Value

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.

Details

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.

References

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

See Also

MMMS

Examples

  # 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)