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Epidemiology. 2006 May;17(3):268-75.

Evaluating short-term drug effects using a physician-specific prescribing preference as an instrumental variable.

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  • 1Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital/Harvard Medical School, Boston, MA 02120, USA. abrookhart@rics.bwh.harvard.edu

Abstract

BACKGROUND:

Postmarketing observational studies of the safety and effectiveness of prescription medications are critically important but fraught with methodological problems. The data sources available for such research often lack information on indications and other important confounders for the drug exposure under study. Instrumental variable methods have been proposed as a potential approach to control confounding by indication in nonexperimental studies of treatment effects; however, good instruments are hard to find.

METHODS:

We propose an instrument for use in pharmacoepidemiology that is based on a time-varying estimate of the prescribing physician's preference for one drug relative to a competing therapy. The use of this instrument is illustrated in a study comparing the effect of exposure to COX-2 inhibitors with nonselective, nonsteroidal antiinflammatory medications on gastrointestinal complications.

RESULTS:

Using conventional multivariable regression adjusting for 17 potential confounders, we found no protective effect due to COX-2 use within 120 days from the initial exposure (risk difference = -0.06 per 100 patients; 95% confidence interval = -0.26 to 0.14). However, the proposed instrumental variable method attributed a protective effect to COX-2 exposure (-1.31 per 100 patients; -2.42 to -0.20) compatible with randomized trial results (-0.65 per 100 patients; -1.08 to -0.22).

CONCLUSIONS:

The instrumental variable method that we have proposed appears to have substantially reduced the bias due to unobserved confounding. However, more work needs to be done to understand the sensitivity of this approach to possible violations of the instrumental variable assumptions.

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