cutpointr: Improved Estimation and Validation of Optimal Cutpoints in R

TL;DR

The paper introduces the cutpointr R package, which provides robust, flexible methods for estimating and validating optimal cutpoints in binary classification, addressing variability and overestimation issues of traditional approaches.

Contribution

The package implements advanced statistical techniques like bootstrapping, smoothing, and user-defined metrics for more reliable cutpoint estimation and validation in binary classification tasks.

Findings

Enhanced stability of cutpoint estimates

Reduced overfitting in performance metrics

Versatile tools for visualization and analysis

Abstract

'Optimal cutpoints' for binary classification tasks are often established by testing which cutpoint yields the best discrimination, for example the Youden index, in a specific sample. This results in 'optimal' cutpoints that are highly variable and systematically overestimate the out-of-sample performance. To address these concerns, the cutpointr package offers robust methods for estimating optimal cutpoints and the out-of-sample performance. The robust methods include bootstrapping and smoothing based on kernel estimation, generalized additive models, smoothing splines, and local regression. These methods can be applied to a wide range of binary-classification and cost-based metrics. cutpointr also provides mechanisms to utilize user-defined metrics and estimation methods. The package has capabilities for parallelization of the bootstrapping, including reproducible random number generation. Furthermore, it is pipe-friendly, for example for compatibility with functions from tidyverse. Various functions for plotting receiver operating characteristic curves, precision recall graphs, bootstrap results and other representations of the data are included. The package contains example data from a study on psychological characteristics and suicide attempts suitable for applying binary classification algorithms.