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

Dongseok Choi, Kim A. Hoffman, Mi Ok Kim, Dennis McCarty

Research output: Contribution to journalArticle

7 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)333-347
Number of pages15
JournalHealth Services Research
Volume48
Issue number1
DOIs
StatePublished - Feb 2013

Keywords

  • Network for the Improvement of Addiction Treatment (NIATx)
  • Wait time
  • process improvement
  • quantile regression

ASJC Scopus subject areas

  • Health Policy

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