Evidence map›Paper›PMID 41058203›Full record

ArticlePharmacoepidemiology and drug safety2025

Potential Bias in Self-Controlled Case Series Design: Impact of Incident Versus Prevalent Scenarios of Exposure and Outcome.

Kyungyeon Jung, Seong Jun Byun, Gyeongmin Lim, Jeong-Eun Lee, Hyesung Lee, Ju Hwan Kim, Ju-Young Shin

Abstract read
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Article in Pharmacoepidemiology and drug safety, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Kyungyeon JungDepartment of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, South Korea.
Seong Jun ByunDreamCIS, Inc, Seoul, South Korea.
Gyeongmin LimDepartment of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, South Korea.
Jeong-Eun LeeDepartment of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, South Korea.
Hyesung LeeDepartment of Medical Informatics, Kangwon National University College of Medicine, Chuncheon, South Korea.
Ju Hwan KimDepartment of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, South Korea.
Ju-Young ShinDepartment of Biohealth Regulatory Science, Sungkyunkwan University, Suwon, South Korea.ORCID https://orcid.org/0000-0003-1010-7525

Funding

Ministry of Science and Technology RS-2024-00405650
6 · The paper itself

Abstract

purposeTo assess changes in study results in self-controlled case series (SCCS) studies and explore potential biases when applying incident and prevalent exposure and outcome scenarios.

methodsWe used the National Health Insurance Service database in South Korea to conduct two SCCS studies: (1) the risk of retinal detachment following fluoroquinolone use (expected elevated risk), and (2) the risk of acute cardiovascular disease following ranibizumab use (expected null association). Exposure and outcome scenarios were classified based on a washout period of 2 and 1 year, respectively, before the observation period: incident exposure-incident outcome, incident exposure-prevalent outcome, prevalent exposure-incident outcome, and prevalent exposure-prevalent outcome. For each scenario, conditional Poisson regression models, adjusted for time-varying age, estimated incidence rate ratios (IRRs) with 95% confidence intervals (CIs).

resultsIn the fluoroquinolone study, IRRs of retinal detachment were as follows: 1.83 (95% CI 1.72-1.96) for incident exposure-incident outcome, 1.83 (1.71-1.95) for incident exposure-prevalent outcome, 1.71 (1.62-1.80) for prevalent exposure-incident outcome, and 1.70 (1.62-1.79) for prevalent exposure-prevalent outcome scenario. In the ranibizumab study, IRRs of acute cardiovascular disease were 0.91 (0.84-0.99), 0.89 (0.82-0.97), 1.02 (0.94-1.10), and 1.00 (0.93-1.08), respectively.

conclusionsIn prevalent exposure scenarios, IRRs in both studies were attenuated, consistent with prevalent user bias. In prevalent outcome scenarios, where survivor bias was anticipated, no notable shifts in IRRs were observed, possibly due to the high severity and low recurrence of the outcomes associated with the respective exposures. Further consideration of prevalent user and survivor bias is important when interpreting SCCS results. Additional research is needed to quantify the impact of varying exposure and outcome definitions on SCCS estimates.

Indexed as

Cardiovascular DiseasesFluoroquinolonesResearch DesignRetinal DetachmentAdultAgedBiasDatabases, FactualFemaleHumansIncidenceMaleMiddle AgedPrevalenceRanibizumabRepublic of KoreaFluoroquinolonesRanibizumabbiasincident user designpharmacoepidemiologyprevalent user designSCCS

Identifiers

PMID41058203
PMCPMC12504913

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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.