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


METHODS
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.


RESULTS
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.
Department of Public Health and Preventive Medicine, Oregon Health & Science University, Portland, OR 97239, USA. choid@ohsu.edu
Dongseok
D
Choi
Kim A
KA
Hoffman
Mi-Ok
MO
Kim
Dennis
D
McCarty
D006801:Humans; D017064:Process Assessment, Health Care; D058996:Quality Improvement / Q000458:organization & administration*; D019966:Substance-Related Disorders / Q000534:rehabilitation*; D013997:Time Factors; D061665:Time-to-Treatment / Q000458:organization & administration*