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

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.

Ischemic Stroke After Bivalent COVID-19 Vaccination: Self-Controlled Case Series Study

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Nationwide safety surveillance of COVID-19 mRNA vaccines following primary series and first booster vaccination in Singapore

“…If infeasible due to volume, chart reviews are performed on a sample of cases to estimate the degree of misclassification from false positive cases, for which adjustments may be performed to yield minimally biased relative incidence (RI) estimates. [8] . Part 2: Observed-over-expected and self-controlled case series analyses …”

Ischemic Stroke after Bivalent COVID-19 Vaccination: A Self-Controlled Case Series Study

“…In analyses of confirmed ischemic stroke events among recipients of bivalent COVID-19 vaccines, we introduced a randomized allocation of confirmed case status to the NBR group. This allocation was guided by the confirmation rates observed among recipients of bivalent COVID-19 vaccines, as outlined by Xu et al 18 Five simulated datasets were generated to replicate the allocation process. SCCS analyses were conducted on each dataset, and the resulting estimates were aggregated using Rubin’s rule, 19 which accounts for both the variability within individual datasets and the variability across the multiple datasets.…”