EffectTreat v1.1
Monthly downloads
Prediction of Therapeutic Success
In personalized medicine, one wants to know, for a given patient and his or her outcome for a predictor (pre-treatment variable), how likely it is that a treatment will be more beneficial than an alternative treatment. This package allows for the quantification of the predictive causal association (i.e., the association between the predictor variable and the individual causal effect of the treatment) and related metrics. Part of this software has been developed using funding provided from the European Union's 7th Framework Programme for research, technological development and demonstration under Grant Agreement no 602552.
Functions in EffectTreat
| Name | Description | |
| Min.R2.delta | Compute minimum \(R^2_{\delta}\) for desired prediction accuracy | |
| GoodPretreatContCont | Examine the plausibility of finding a good pretreatment predictor in the Continuous-continuous case | |
| Example.Data | An example dataset | |
| Min.Max.Multivar.PCA | Minimum and maximum values for the multivariate predictive causal association (PCA) in the continuous-continuous case | |
| Predict.Treat.Multivar.ContCont | Compute the predicted treatment effect on the true endpoint of a patient based on his or her observed vector of pretreatment predictor values in the continuous-continuous setting | |
| CausalPCA.ContCont | Show a causal diagram of the median correlation between the counterfactuals in the continuous-continuous setting | |
| Multivar.PCA.ContCont | Compute the multivariate predictive causal association (PCA) in the Continuous-continuous case | |
| PCA.ContCont | Compute the predictive causal association (PCA) in the Continuous-continuous case | |
| Predict.Treat.T0T1.ContCont | Compute the predicted treatment effect on the true endpoint of a patient based on his or her observed pretreatment predictor value in the continuous-continuous setting for a particular (single) value of \(\rho_{T0T1}\). | |
| plot GoodPretreatContCont | Graphically illustrates the theoretical plausibility of finding a good pretreatment predictor in the continuous-continuous case | |
| Predict.Treat.ContCont | Compute the predicted treatment effect on the true endpoint of a patient based on his or her observed pretreatment predictor value in the continuous-continuous setting | |
| plot.Predict.Treat.ContCont | Plots the distribution of the individual causal effect based on \(S\). | |
| plot PCA.ContCont | Plots the Predictive Causal Association in the continuous-continuous case | |
| plot Min.R2.delta | Plot \(R^2_{\delta}\) as a function of \(\delta\). | |
| summary | Summary | |
| plot.Predict.Treat.T0T1.ContCont | Plots the distribution of the individual causal effect based on \(S\) for a specific assumed correlation between the counterfactuals. | |
| No Results! | ||
Last month downloads
Details
| Type | Package |
| License | GPL (>= 2) |
| Repository | CRAN |
| NeedsCompilation | no |
| Packaged | 2020-07-04 08:24:39 UTC; wim |
| Date/Publication | 2020-07-04 21:30:03 UTC |
| imports | methods |
| Contributors | Wim der Elst, Ariel Alonso, Geert Molenberghs |
Include our badge in your README
[](http://www.rdocumentation.org/packages/EffectTreat)