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Xu S, Clarke CL, Newcomer SR, Daley MF, Glanz JM. Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample. Biom J 2018; 60:748-760. [PMID: 29768667 DOI: 10.1002/bimj.201700088] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2017] [Revised: 01/08/2018] [Accepted: 01/10/2018] [Indexed: 01/10/2023]
Abstract
Vaccine safety studies are often electronic health record (EHR)-based observational studies. These studies often face significant methodological challenges, including confounding and misclassification of adverse event. Vaccine safety researchers use self-controlled case series (SCCS) study design to handle confounding effect and employ medical chart review to ascertain cases that are identified using EHR data. However, for common adverse events, limited resources often make it impossible to adjudicate all adverse events observed in electronic data. In this paper, we considered four approaches for analyzing SCCS data with confirmation rates estimated from an internal validation sample: (1) observed cases, (2) confirmed cases only, (3) known confirmation rate, and (4) multiple imputation (MI). We conducted a simulation study to evaluate these four approaches using type I error rates, percent bias, and empirical power. Our simulation results suggest that when misclassification of adverse events is present, approaches such as observed cases, confirmed case only, and known confirmation rate may inflate the type I error, yield biased point estimates, and affect statistical power. The multiple imputation approach considers the uncertainty of estimated confirmation rates from an internal validation sample, yields a proper type I error rate, largely unbiased point estimate, proper variance estimate, and statistical power.
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Affiliation(s)
- Stanley Xu
- The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, 80231, USA.,School of Public Health, University of Colorado, Aurora, CO, 80045, USA
| | - Christina L Clarke
- The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, 80231, USA
| | - Sophia R Newcomer
- The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, 80231, USA.,School of Public Health, University of Colorado, Aurora, CO, 80045, USA
| | - Matthew F Daley
- The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, 80231, USA.,Department of Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Jason M Glanz
- The Institute for Health Research, Kaiser Permanente Colorado, Denver, CO, 80231, USA.,School of Public Health, University of Colorado, Aurora, CO, 80045, USA
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