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Requests to consider #41
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On addition. Please consider adding a function to output the AUPEC plot. Thanks. |
Dear @Rikkkkkin, Thank you very much for these suggestions and we will be working on them now. We will let you know when these changes are implemented. Best regards, |
Hi @Rikkkkkin, Thanks for reaching out. We have a new version of evalITR package under development. It's not on CRAN yet but it has already incorporated many features you suggested. You can install it from GitHub with:
Please feel free to test it out if it fits your needs. We would really appreciate any feedback you might have on the new development!
If you encounter any problems running the new version of the package, please feel free to reply to this thread and we are happy to help! Jialu |
Thank you so much, these are very helpful. Especially plotting AUPEC is very informative. • Suppose a new drug A had on average better outcome compared with previous drug B; i.e. E[Y(1)] > E[Y(0)] . Then the guideline recommends treating every patient with drug A. However, since drug A is newer and more costful, a practical choice is whether we can select patients who can be treated with drug B. If E[Y(g(X))] > E[Y(1)] where g(X) indicates an ITR, then adopting the choice is rationalized, so this comparison is of interest. • In practice, we need to select those who need drug A, and contrasting the GATEs with a simple cutoff of zero (not by quartile etc) is informative. • Thank you for integrating DML. I would like to check whether the first cross-validation is as follows, to maximize the efficiency: in R-learner, first data splitting is to develop nuisance model (step1) and calculate residuals (step2). If naively split data into half, residuals are calculated in only half sample size, which is not desirable. If do cross-validation, say 5-fold, residuals are calculated in every sample. For example in causalforest() function, we cannot specify such cross-validation, and it is generally not enough efficient in medical RCT data. I am currently manually coding such, while I thought it very helpful if your function integrates it. Thanks for your consideration. Rik |
@Rikkkkkin We have released a brand new version of evalITR v1.0.0 that incorporated many of the comments you mentioned above, and also offers new functionality to integrate training and evaluation of ITRs in one step. We hope you can check it out and let us know! |
Hi Michael,
I am writing a paper describing proper approaches to ITR-related research in epidemiology, in which your package will be promoted. I would like to suggest some functions that are relevant to applied research.
• Comparison of average outcome under ITR versus treat everyone: I understand the concept of PAPE but applied researchers are primary interested in this comparison.
• GATE-like comparison of those with CATE<0 and >0: Although the RCT population is not representative, this comparison is a better illustration for applied researchers.
• Integration of DML (DR-learner or R-learner): For these approaches needing additional cross-validation, is it okay just to use this package by estimating CATE for all samples just like T-learner (i.e. if we further split the data within the cross-fitting steps)?
Hope these can be integrated in the next version. Thanks for your consideration.
Rik
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