# 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

1 2 | ```
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 |

`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. |

### 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.

### Value

A list with the following elements:

`fit` |
The |

`lam.best` |
The optimal |

`fit.selected` |
An object returned by |

`sfit` |
An object returned by |

### Author(s)

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

Contributors: Brigid M. Wilson, Dilan C. Paranagama

Maintainer: Lin Li <lli@biostatsolutions.com>

### 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

1 2 3 4 5 6 |