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Abstract #53421 Published in IGR 15-2
Case-crossover studies of therapeutics: design approaches to addressing time-varying prognosis in elderly populations
Wang SV; Gagne JJ; Glynn RJ; Schneeweiss SEpidemiology (Cambridge, Mass.) 2013; 24: 375-378
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.
Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA 02120, USA. swang27@partners.org
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