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Health Serv Res. 2013 Feb;48(1):333-47. doi: 10.1111/j.1475-6773.2012.01436.x. Epub 2012 Jun 20.

A high-resolution analysis of process improvement: use of quantile regression for wait time.

Author information

1
Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, OR 97239, USA. choid@ohsu.edu

Abstract

OBJECTIVE:

Apply quantile regression for a high-resolution analysis of changes in wait time to treatment and assess its applicability to quality improvement data compared with least-squares regression.

DATA SOURCE:

Addiction treatment programs participating in the Network for the Improvement of Addiction Treatment.

METHODS:

We used quantile regression to estimate wait time changes at 5, 50, and 95 percent and compared the results with mean trends by least-squares regression.

PRINCIPAL FINDINGS:

Quantile regression analysis found statistically significant changes in the 5 and 95 percent quantiles of wait time that were not identified using least-squares regression.

CONCLUSIONS:

Quantile regression enabled estimating changes specific to different percentiles of the wait time distribution. It provided a high-resolution analysis that was more sensitive to changes in quantiles of the wait time distributions.

PMID:
22716460
PMCID:
PMC3449006
DOI:
10.1111/j.1475-6773.2012.01436.x
[Indexed for MEDLINE]
Free PMC Article

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