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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Outlier classification performance of risk adjustment methods when profiling multiple providers.

Timo B Brakenhoff¹, Kit C B Roes², Karel G M Moons²

¹Julius Center for Health Sciences and Primary CareUniversity Medical Center Utrecht, PO Box 85500, Utrecht, 3508 GA, the Netherlands. T.B.Brakenhoff-2@umcutrecht.nl.

BMC Medical Research Methodology

|June 16, 2018

Summary

This summary is machine-generated.

Propensity score (PS) adjustment is a viable alternative to random effects logistic regression for accurately classifying health care provider quality. This method improves outlier classification performance when profiling multiple providers, reducing misclassification risks.

Keywords:

Classification Logistic regression Profiling Propensity score Random effects Risk adjustment Simulation study

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Area of Science:

Health Services Research
Biostatistics
Health Care Quality

Background:

Accurate health care provider quality assessment requires case-mix adjustment.
Misclassification of provider performance can lead to serious consequences.
Propensity score (PS) methods offer potential alternatives to traditional regression for risk adjustment.

Purpose of the Study:

To evaluate the outlier classification performance of various risk adjustment methods.
To compare propensity score (PS) methods against logistic regression for provider profiling.

Main Methods:

A simulation study using empirical data was conducted.
Evaluated logistic regression (fixed and random effects), PS adjustment, and three PS weighting methods.
Assessed classification accuracy using measures like sensitivity and specificity under varying parameters.

Main Results:

Fixed effects logistic regression showed high sensitivity and negative predictive value but low specificity and positive predictive value.
PS adjustment and random effects logistic regression demonstrated comparable or superior performance across all classification measures.
Other PS weighting methods performed less effectively compared to PS adjustment and random effects logistic regression.

Conclusions:

Propensity score (PS) adjustment is a viable alternative to random effects logistic regression for profiling multiple providers.
PS adjustment demonstrated robust performance across various scenarios in outlier classification.
The findings support the use of PS adjustment for more accurate health care provider quality assessment.