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JMP Wish List

We want to hear your ideas for improving JMP software.

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We consider several factors when looking for what ideas to add to JMP. This includes what will have the greatest benefit to our customers based on scope, needs and current resources. Product ideas help us decide what features to work on next. Additionally, we often look to ideas for inspiration on how to add value to developments already in our pipeline or enhancements to new or existing features.

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Decision Curve Analysis

Status: New Submitted by on ‎11-07-2023 10:13 AM
Comment

What inspired this wish list request? Please describe the current issue that needs improvement or the problem to be solved that is not easy or possible right now, with an example use case. 

I recently ran into some reviewers requesting that we follow the methodologies described in: Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) described in the Annals of Internal Medicine 2015; 162: W1-W73 paper. In that paper the authors described depiction of predicted outcomes (x axis) vs actual outcomes (y axis) using a calibration plot. They go on to describe net benefit curves (A Simple, Step by Step Guide to Interpreting Decision Curve Analysis; Vickers, van Calster, and Steyerberg; Diagnostic and Prognostic Research, 2019; https://doi.org/10.1186/s41512-019-0064-7) as a tool to assess benefit vs threshold risk

 

A tutorial and code for this tool is available online for Python, R, STATA, and SAS: Decision Curve Analysis Tutorial (danieldsjoberg.com)

 

This tool extends the utility of existing tools used to assess the accuracy of predictive models such as AUC (c-statistic) by allowing for the application of clinical utility. It is a tool that is increasingly used in business and other risk-benefit settings. Although output can be tabular, this this is primarily a graphic-based tool that 'fits' nicely within the JMP paradigm.

 

Current JMP version: JMP Pro 17.2