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

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.

© Health Research and Educational Trust.

Figures

Figure 1

The Distributions of Wait Time to Treatment in Program 8. (A) Density Plot of All Wait Time. (B) Density Plot of Wait Time in the First (n = 39), Mid (n = 49) and Last Months (n = 33)

Figure 2

Guidelines How to Choose between Least-Squares Regression and Quantile Regression

Figure 3

Quantile Regression Analysis of Wait Time of Three Intensive Outpatient Programs. Solid Lines Are Estimated 5, 50, and 95 percent Quantile Curves Using Quantile Regression (QR), and Dashed Lines Are the Estimated Mean Curve and 90 percent Prediction Bands Using Least-Squares Regression (LS). (A) Program 5, (B) Program 8, and (C) Program 2