MMMS: Subgroup identification using a multi-marker molecular...
In MMMS: Multi-Marker Molecular Signature for Treatment-specific Subgroup Identification

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

Identification of a treatment-specific subgroup for time-to-event outcomes using a multi-marker molecular signature approach

1 2	MMMS(time, event, treat, bio, covar = NULL, pct.lb = 20, pct.ub = 80, n.boot = 1000, pos.direction = FALSE, nfolds = 5, alpha = 0.5, verbose = FALSE, seed = NULL)

`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.
`pct.lb`	A scalar indicating the lower bound of the search range for desired subgroup sizes in percentage (e.g. 20 means 20%).
`pct.ub`	A scalar indicating the upper bound of the search range for desired subgroup sizes in percentage (e.g. 80 means 80%).
`n.boot`	A scalar indicating the number of bootstraps for calculating the bootstrap p-value for the subgroup effect. Default is 1000, which is a recommended value.
`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.
`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=ridige, 1=lasso). A fixed value is supposed to be used, without searching for the optimal alpha value. Default is 0.5.
`verbose`	A logical value indicating whether bootstrap progress should be printed. Default is `FALSE`.
`seed`	An integer for setting random seed, if provided. Default is `NULL` for not setting any seed.

MMMS() calls several functions that could also be used separately: get.score(), get.score.main(), get.subgroup(), etc.

As is described in Li et al. (2014), the bootstrap p-value is based on a statistically valid test whose type I error is approximately controlled at the nominal level. However, caution is needed for interpreting the estimates of subgroup size and treatment-by-subgroup interaction effect, as bias has been observed in these estimates. Approaches for correcting bias in the estimates may be implemented in future versions of the “MMMS” package.

A list with the following elements:

`score.obj`	The object returned by `get.score()`.
`score`	The composite scores based on interaction terms.
`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.
`lambda`	The optimal `lambda` value chosen for obtaining the MMMS composite scores.
`alpha`	The `alpha` value used for obtaining the MMMS composite scores.
`subgrp.obj`	The object returned by `get.subgroup()`.
`subgrp.size`	The size (in percentage) of the optimal subgroup identified.
`subgrp.fit`	The fitted model object for the optimal subgroup identified.
`subgrp.cut`	The cutpoint of the composite score `score` for the optimal subgroup identified.
`subgrp.pval`	The p-value of the treatment-by-subgroup effect based on `n.boot` bootstraps. `NA` is returned if `n.boot=0`.
`n.boot`	The number of bootstraps considered for calculating `subgrp.pval`.

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

get.score, get.subgroup.

# load the dataset
data(simdat)
    
# estimate the MMMS (No bootstrap is considered for a quick illustration)
mmms = with(simdat,MMMS(time,event,treat,bio,covar,pct.lb=20,pct.ub=80,n.boot=0,
                        pos.direction=FALSE,nfolds=5,alpha=0.5,verbose=TRUE,seed=12345))