dipm: an R package implementing the Depth Importance in Precision Medicine (DIPM) tree and Forest-based method

Area of Science:

• Biostatistics
• Computational Biology
• Precision Medicine

Background:

• Precision medicine aims to tailor treatments to individual patients.
• Identifying patient subgroups with differential treatment responses is crucial for effective precision medicine.
• Existing methods may not fully capture the nuances of subgroup identification for treatment effects.

Purpose of the Study:

• Introduce the novel R package, dipm.
• Implement the Depth Importance in Precision Medicine (DIPM) classification tree method.
• Facilitate the identification of relevant subgroups in precision medicine settings.

Main Methods:

• The DIPM method utilizes a classification tree approach.
• The dipm R package provides an implementation of the DIPM method.
• The package integrates R code with a C program for efficient computation.

Main Results:

• The dipm package offers a robust tool for subgroup discovery.
• Enables identification of patient subgroups with distinct treatment outcomes.
• Supports the application of precision medicine strategies.

Conclusions:

• The dipm R package effectively implements the DIPM method.
• Provides a valuable resource for researchers in precision medicine.
• Aids in identifying patient subgroups for optimized treatment assignment.