ArticleAmerican journal of epidemiology2014
"First-wave" bias when conducting active safety monitoring of newly marketed medications with outcome-indexed self-controlled designs.
Article in American journal of epidemiology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 23 citations in OpenAlex.
- Control yourself: ISPE-endorsed guidance in the application of self-controlled study designs in pharmacoepidemiology.Pharmacoepidemiology and drug safety · 2021Pooled it
- Self-controlled designs in pharmacoepidemiology involving electronic healthcare databases: a systematic review.BMC medical research methodology · 2017Pooled it
- The Extent and Magnitude of Bias in Case-Crossover Studies of Real-World Non-transient Medications Patterns: A Simulation Study with Real-World Examples.Drug safety · 2026Article
- Sequential Epidemiological Analyses of Real-World Data: A Tool for Prospective Drug Safety Surveillance from the Rofecoxib Example.Drug safety · 2025Article
- Risk of Fracture Associated with Pregabalin or Mirogabalin Use: A Case-Case-Time-Control Study Based on Japanese Health Insurance Claims Data.Drug safety · 2025Article
- Thinking Three-Dimensionally: A Self- and Externally-Controlled Approach to Screening for Drug-Drug-Drug Interactions Among High-Risk Populations.Clinical pharmacology and therapeutics · 2024Article
- High-Throughput Computing to Automate Population-Based Studies to Detect the 30-Day Risk of Adverse Outcomes After New Outpatient Medication Use in Older Adults with Chronic Kidney Disease: A Clinical Research Protocol.Canadian journal of kidney health and disease · 2024Article
- Bias Due to Within-Subject Exposure Dependency With or Without Bias Due to Lack of Pairwise Exchangeability When Exposure Is Chronic in Case-Crossover and Case-Time-Control Studies: A Simulation Study.American journal of epidemiology · 2023Article
- Drug-Drug Interaction Surveillance Study: Comparing Self-Controlled Designs in Five Empirical Examples in Real-World Data.Clinical pharmacology and therapeutics · 2021Article
- Bias in case-crossover studies of medications due to persistent use: A simulation study.Pharmacoepidemiology and drug safety · 2020Article
- Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic.Epidemiology (Cambridge, Mass.) · 2018Article
- Using Simulated Data to Assess Case-Crossover Designs for Studying Less Transient Effects of Drugs.Drug safety · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.