node_dipm function

Panel-Generator for Visualization of A Precision Medicine Tree

Panel-Generator for Visualization of A Precision Medicine Tree

This function provides a new plot method for dipm

and spmtree. It visualizes stratified treatment groups through boxplots for a continuous outcome and survival plots for a survival outcome, respectively.

node_dipm(obj, ...)

Arguments

  • obj: A party tree object returned from either the dipm() or spmtree() function
  • ...: Arguments passed on to plotfun

Returns

No return value, called for plot

Details

This function visualizes the precision medicine trees proposed in Chen and Zhang (2020a, b).

Examples

#' # # ... an example with a continuous outcome variable # and two treatment groups # N = 100 set.seed(123) # generate binary treatments treatment = rbinom(N, 1, 0.5) # generate candidate split variables X1 = rnorm(n = N, mean = 0, sd = 1) X2 = rnorm(n = N, mean = 0, sd = 1) X3 = rnorm(n = N, mean = 0, sd = 1) X4 = rnorm(n = N, mean = 0, sd = 1) X5 = rnorm(n = N, mean = 0, sd = 1) X = cbind(X1, X2, X3, X4, X5) colnames(X) = paste0("X", 1:5) # generate continuous outcome variable calculateLink = function(X, treatment){ ((X[, 1] <= 0) & (X[, 2] <= 0)) * (25 * (1 - treatment) + 8 * treatment) + ((X[, 1] <= 0) & (X[, 2] > 0)) * (18 * (1 - treatment) + 20 * treatment) + ((X[,1 ] > 0) & (X[, 3] <= 0)) * (20 * (1 - treatment) + 18 * treatment) + ((X[,1] > 0) & (X[,3] > 0)) * (8 * (1 - treatment) + 25 * treatment) } Link = calculateLink(X, treatment) Y = rnorm(N, mean = Link, sd = 1) # combine variables in a data frame data = data.frame(X, Y, treatment) # fit a dipm classification tree tree = dipm(Y ~ treatment | ., data, mtry = 1, maxdepth = 3) plot(tree, terminal_panel = node_dipm)

References

Chen, V., Li, C., and Zhang, H. (2022). dipm: an R package implementing the Depth Importance in Precision Medicine (DIPM) tree and Forest-based method. Bioinformatics Advances, 2 (1), vbac041.

Chen, V. and Zhang, H. (2020). Depth importance in precision medicine (DIPM): a tree and forest based method. In Contemporary Experimental Design, Multivariate Analysis and Data Mining, 243-259.

Chen, V. and Zhang, H. (2022). Depth importance in precision medicine (DIPM): A tree-and forest-based method for right-censored survival outcomes. Biostatistics 23 (1), 157-172.

Seibold, H., Zeileis, A., and Hothorn, T. (2019). model4you: An R package for personalised treatment effect estimation. Journal of Open Research Software 7 (1).

Hothorn, T. and Zeileis, A. (2015). partykit: a modular toolkit for recursive partytioning in R. The Journal of Machine Learning Research

16 (1), 3905-3909.

See Also

dipm, spmtree

  • Maintainer: Cai Li
  • License: GPL (>= 2)
  • Last published: 2022-10-27

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