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Case-crossover Studies of Therapeutics: Design Approaches to Addressing Time-varying Prognosis in Elderly Populations

Wang, Shirley V.; Gagne, Joshua J.; Glynn, Robert J.; Schneeweiss, Sebastian

Epidemiology:
doi: 10.1097/EDE.0b013e31828ac9cb
Pharmacoepidemiology
Abstract

Background: Self-controlled analysis methods implicitly adjust for time-invariant confounding within individuals. A person’s prognosis often varies over time and affects both therapy choice and subsequent health outcomes. Current approaches may not be able to fully address this within-person confounding. We evaluated the potential impact of time-varying prognosis in self-controlled studies of treatment effects and the extent to which alternative adjustment strategies could mitigate these biases.

Methods: We used Medicare data linked to prescription drug data from a pharmaceutical assistance program to conduct case-crossover studies of the relationship between intermittent use of five classes of preventive medications (statins, oral hypoglycemics, antihypertensives, osteoporosis, and glaucoma medications) and death—relationships that are strongly biased because of healthy-user and sick-stopper effects. We used the case-case time-control design to adjust for confounding from exposure trends related to prognosis. Each class of medications was evaluated separately, with the remaining four used as reference drugs to estimate prognosis-related exposure trends.

Results: The case-crossover odds ratios were 0.39, 0.38, 0.40, 0.39, and 0.45 for statin, antihypertensive, glaucoma, hypoglycemic, and osteoporosis drugs, respectively. After adjusting for the estimated noncausal prognosis-related trends in drug exposure among all eligible cases, odds ratios were clustered closer to null (0.99, 0.95, 1.02, 0.99, and 1.16, respectively).

Conclusions: Consideration of the sociology of medication use leading to health outcomes is essential in designing and analyzing self-controlled studies of treatment effects. Although the case-case time-control design was able to reduce bias from prognosis-related exposure trends in our examples, the difficulty in identifying appropriate reference exposures could be prohibitive.

Author Information

From the Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA.

Supplemental digital content is available through direct URL citations in the HTML and PDF versions of this article (www.epidem.com). This content is not peer-reviewed or copy-edited; it is the sole responsibility of the author.

Correspondence: Shirley Wang, Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, 1620 Tremont St, Suite 3030, Boston, MA 02120. E-mail: swang27@partners.org.

Received July 2, 2012

Accepted December 11, 2012

© 2013 by Lippincott Williams & Wilkins, Inc