By Stuart G. Baker, 2015
Introduction
A predictive marker is a baseline variable in a randomized trial that is used to determine subgroups in which the effect of treatment is greater than average. This software uses a modified adaptive signature design to evaluate a randomized trial with a binary outcome and multiple baseline variables (possibly high dimensional). The software splits the data into training and test samples. A key option is a single split or multiple random splits. For the training sample the software fits various benefit functions. For the test sample the software computes benefit scores based on the benefit function and treatment effect in subgroups with benefit scores greater than cutpoints. The software plots estimated treatment effect versus cutpoint, which is similar to a tail-oriented subpopulation treatment effect pattern plot.
This software corresponds to
Baker SG and Bonetti M. Evaluating Markers for Guiding Treatment J. Natl. Cancer Inst. 2016 108: djw101
https://academic.oup.com/jnci/article/108/9/djw101/2499565.
Requirement: Mathematica Version 8 or later.
Set-Up
| copy | all files into some folder called "FOLDER" |
|---|---|
| start | a new Mathematica session |
| type | SetDirectory["FOLDER"] |
| type | << markerfit.m |
To run on hypothetical data,
type TrialFit[datasim, NewFitQ->True]
To try on your own data,
type TrialFit[ dataset, options]
Options
| Option | Default | Explanation |
|---|---|---|
| NewFitQ | True | New fitting or use stored result of previous fit |
| FractionSplit | 0.5 | Fraction split into test sample |
| ModelSet | "Basic" | Basic is 1, 2, 5 markers |
| MinimumGroupSize | 20 | Minimum sample size of training-test sample |
| Num Cut | 8 | Number of cutpoints in test sample |
| ShowProgQ | False | Show progress of fitting algorithm |
| ShowTabQ | False | Show data and results tables |
| MaxBoot | 20 | Number of bootstrap iterations |
| PlotFormat | "quantiles" | Format of horizontal axis on plot |
dataset ={x0,x1,y0,y1,xname,datasetname}
| x0 | n x g matrix of baseline variables for randomization group 0 |
|---|---|
| x1 | n x g matrix of baseline variables for randomization group 1 |
| y0 | a length n list of binary outcomes (0 or 1) for randomization group 0 |
| y1 | a length n list of binary outcomes (0 or 1) for randomization group 1 |
| xname | a length g list of names of baseline variables |
| datasetname | name of dataset |
File Contents
Download All (ZIP, 43 KB)
| File name | Description |
|---|---|
| markerfit.m | calls all files |
| markerfitcadit.m | Cadit |
| markerfitcadittest.m | Cadit: test sample |
| markerfitcadittrain.m | Cadit: training sample |
| markerfitinputcheck | Checks input |
| markerfitmodel.m | fit models |
| markerfitsplit.m | Split data into training and test samples |
| markerfitlogit.m | Fit logistic regression |
| markerfitplot.m | Plot results |
| markerfitboot.m | Bootstrap test sample |
| markerfitbootci.m | Boostrap confidence interval |
| markerfitrd.m | Risk difference |
| markerfitrdtrain.m | Risk difference: training sample |
| markerfitresp.m | Responder only |
| markerfitresptrain.m | Responder only: training sample |
| markerfitresptest.m | Responder only: test sample |
| markerfitsim.m | Generate simulated data |
Disclaimer
This code is provided "as is", without warranty of any kind, express or implied, including but not limited to the warranties of merchantability, fitness for a particular purpose and non-infringement. In no event shall the NCI or the individual developers be liable for any claim, damages or other liability of any kind. Use of this code by recipient is at recipient's own risk. NCI makes no representations that the use of the code will not infringe any patent or proprietary rights of third parties.
Last updated: February 03, 2016