MMMS (version 0.1)

get.score.main: Calculation of composite scores based on a main-effect model

Description

This function is to fit a main-effect model assuming no treatment-specific subgroups exist (under the null).

Usage

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

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.

Value

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.

Details

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.

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, get.score

Examples

  # 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)