Display Settings:


Send to:

Choose Destination
See comment in PubMed Commons below
Stat Med. 2005 Mar 15;24(5):791-816.

The use of quantile regression in health care research: a case study examining gender differences in the timeliness of thrombolytic therapy.

Author information

  • 1Institute for Clinical Evaluative Sciences, Canada. peter.austin@ices.on.ca


Investigators are frequently interested in determining patient and system characteristics associated with delays in the provision of essential medical treatment. Investigators have typically used either multiple linear regression or Cox proportional hazards models to assess the impact of patient and system characteristics on the timeliness of medical treatment. A drawback to the use of these two methods is that they allow, at best, a partial exploration of how a distribution of delays in treatment or of waiting times changes with patient characteristics. In contrast, quantile regression models allow one to assess how any quantile of a conditional distribution changes with patient characteristics. We illustrate the utility of quantile regression by examining gender differences in the delivery of thrombolysis in patients with an acute myocardial infarction. We demonstrate that richer inferences can be drawn through the use of quantile regression. Females were more likely to experience delays in treatment compared to males. Furthermore, gender had a greater impact upon those patients who had the greatest delays in treatment. Investigators who want to determine how a distribution of delays in treatment or of waiting times changes with patient or system characteristics should consider complementing their analyses with the use of quantile regression.

Copyright (c) 2004 John Wiley & Sons, Ltd.

[PubMed - indexed for MEDLINE]
PubMed Commons home

PubMed Commons

How to join PubMed Commons

    Supplemental Content

    Full text links

    Icon for John Wiley & Sons, Inc.
    Loading ...
    Write to the Help Desk