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Updated: May 14, 2026

An R-Based Landscape Validation of a Competing Risk Model

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

A description of mixed group validation.

Paul A Jewsbury¹, Stephen C Bowden²

¹University of Melbourne, Parkville, Victoria, Australia jewsbury@unimelb.edu.au.

|January 31, 2013

Summary

This summary is machine-generated.

Mixed group validation (MGV) estimates diagnostic accuracy without a perfect criterion. This statistical model offers an alternative to known group validation (KGV) when perfect criteria are unavailable.

Keywords:

construct validity criterion-related validity mixed group validation sensitivity specificity

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Area of Science:

Statistics
Psychometrics
Diagnostic Accuracy

Background:

Mixed group validation (MGV) is a statistical model for estimating diagnostic accuracy.
Unlike known group validation (KGV), MGV does not necessitate a perfect external validity criterion.
This study details MGV, including its standard error, assumption violation effects, and sample size recommendations.

Purpose of the Study:

To describe the statistical model of Mixed Group Validation (MGV).
To analyze the standard error and effects of assumption violation in MGV.
To provide recommendations for sample sizes and identify conditions for detecting assumption violations.

Main Methods:

Specification of standard error for MGV validity estimates.
Analysis of the impact of assumption violation on MGV.
Determination of required sample sizes for various study conditions.
Evaluation of methods for identifying assumption violation.
Simulation of MGV with imperfect base rate estimates.

Main Results:

MGV estimates have a wider margin of error compared to KGV.
MGV performance is optimal when the research design closely resembles KGV.
The impact of assumption violation is contingent on its severity and base rate values.
Assumption violation in MGV is detectable only in severe instances.

Conclusions:

MGV provides a valuable alternative for diagnostic accuracy estimation when perfect criteria are absent.
Understanding the limitations and conditions for assumption violation is crucial for reliable MGV application.
MGV requires careful consideration of sample size and potential assumption violations for accurate results.