EffectTreat v1.1

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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.
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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

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