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Updated: May 21, 2026

ScanLag: High-throughput Quantification of Colony Growth and Lag Time
Published on: July 15, 2014
A high-resolution analysis of process improvement: use of quantile regression for wait time
Dongseok Choi1, Kim A Hoffman, Mi-Ok Kim
1Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, OR 97239, USA. choid@ohsu.edu
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.
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