title: "First-wave" bias when conducting active safety monitoring of newly marketed medications with outcome-indexed self-controlled designs.
abstract: Large health care databases are used extensively for pharmacoepidemiologic studies. Unique methodological issues arise when applying self-controlled designs (i.e., using within-person comparisons) for active surveillance of newly marketed drugs. We use 3 examples to illustrate bias related to population-level exposure time trends when using outcome-indexed self-controlled (i.e., case-crossover) designs for active surveillance and evaluate the ability of the case-time-control design to adjust for bias from population-level exposure time trends. We mimicked active surveillance by conducting sequential analyses after market entry for 3 medications and outcomes (valdecoxib for myocardial infarction (MI), aripiprazole for MI, and telithromycin for acute liver failure) using Medicaid Analytic eXtracts (from all 50 US states, 2000-2006). The case-crossover exposure odds ratio (EOR) in the months immediately following valdecoxib market entry implausibly suggested a 12-fold higher risk of MI during exposed time relative to unexposed time; among age-, sex-, and time-matched controls, the corresponding EOR of 4.5 indicated strong population-level exposure time trends. Over subsequent monitoring periods, case-crossover EORs rapidly dropped to 1.4. Adjustment for bias from population-level exposure time trends with the case-time-control analysis resulted in more consistent associations between valdecoxib and MI across sequential monitoring periods. Similar results were observed in each example. Strong population-level exposure time trends can bias case-crossover studies conducted among "first-wave" users of newly marketed medications. Suggested strategies can help assess and adjust for population-level exposure time trends.
authors.forename: Shirley V
authors.initials: SV
authors.lastname: Wang
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
authors.forename: Malcolm
authors.initials: M
authors.lastname: Maclure
authors.forename: Joshua J
authors.initials: JJ
authors.lastname: Gagne
mesh_terms: D000900:Anti-Bacterial Agents / Q000009:adverse effects; D014150:Antipsychotic Agents / Q000009:adverse effects; D000068180:Aripiprazole; D015982:Bias; D016022:Case-Control Studies; D018592:Cross-Over Studies; D052246:Cyclooxygenase 2 Inhibitors / Q000009:adverse effects; D062313:Databases, Pharmaceutical; D006801:Humans; D007555:Isoxazoles / Q000009:adverse effects; D048628:Ketolides / Q000009:adverse effects; D040541:Marketing / Q000592:standards / Q000706:statistics & numerical data; D008484:Medicaid / Q000706:statistics & numerical data; D017891:Pharmacoepidemiology / Q000379:methods* / Q000592:standards; D010879:Piperazines / Q000009:adverse effects; D011358:Product Surveillance, Postmarketing / Q000592:standards* / Q000706:statistics & numerical data*; D015363:Quinolones / Q000009:adverse effects; D013449:Sulfonamides / Q000009:adverse effects; D014481:United States; D000096926:Benzenesulfonamides
chemical_list: D000900:Anti-Bacterial Agents; D014150:Antipsychotic Agents; D000068180:Aripiprazole; D052246:Cyclooxygenase 2 Inhibitors; D007555:Isoxazoles; D048628:Ketolides; D010879:Piperazines; D015363:Quinolones; D013449:Sulfonamides; C406224:valdecoxib; D000096926:Benzenesulfonamides; C106791:telithromycin
keywords: bias; crossover design; drug surveillance, postmarketing; epidemiologic methods; epidemiologic monitoring
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