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

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


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • Humans
  • Process Assessment, Health Care
  • Quality Improvement / organization & administration*
  • Substance-Related Disorders / rehabilitation*
  • Time Factors
  • Time-to-Treatment / organization & administration*