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. 2018 Jul;60(4):748-760.
doi: 10.1002/bimj.201700088. Epub 2018 May 16.

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample

Stanley Xu  1   2 , Christina L Clarke  1 , Sophia R Newcomer  1   2 , Matthew F Daley  1   3 , Jason M Glanz  1   2
Affiliations

Analyzing self-controlled case series data when case confirmation rates are estimated from an internal validation sample

Stanley Xu et al. Biom J. 2018 Jul.

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.

Keywords: confirmation rate of cases; internal validation sample; multiple imputation; self-controlled case series; vaccine safety.

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Conflict of interest statement

CONFLICT OF INTEREST

The authors have declared no conflict of interest.

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References

    1. Baggs J, Gee J, Lewis E, Fowler G, Benson P, Lieu T, … Weintraub E (2011). The Vaccine Safety Datalink: A model for monitoring immunization safety. Pediatrics, 127 (Suppl 1), S45–S53. - PubMed
    1. Chubak J, Pocobelli G, & Weiss NS (2012). Tradeoffs between accuracy measures for electronic health care data algorithms. Journal of Clinical Epidemiology, 65 (3), 343–349. - PMC - PubMed
    1. Cole SR, Chu H, & Greenland S (2006). Multiple-imputation for measurement-error correction. International Journal of Epidemiology, 35 (4), 1074–1081. - PubMed
    1. Curtis LH, Weiner MG, Boudreau DM, Cooper WO, Daniel GW, Nair VP, … Brown JS (2012). Design considerations, architecture, and use of the mini-sentinel distributed data system. Pharmacoepidemiology and Drug Safety, 21 (Suppl. 1), 23–31. - PubMed
    1. Daley MF, Clarke CL, Glanz JM, Xu S, Hambidge SJ, Donahue JG, … Weintraub E (2018). The safety of live attenuated influenza vaccine in children and adolescents 2 through 17 years of age: A Vaccine Safety Datalink study. Pharmacoepidemiology and Drug Safety, 27 (1), 59–68. - PMC - PubMed

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