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

Details

• Alternative Title:
  Biom J
• Personal Author:
  Xu, Stanley ; Clarke, Christina L. ; Newcomer, Sophia R. ; Daley, Matthew F. ; Glanz, Jason M.
• Description:
  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.
• Subjects:
  Adolescent Algorithms Article Biometry Case-Control Studies Child Child, Preschool Confirmation Rate Of Cases Electronic Health Records Humans Infant Internal Validation Sample Monte Carlo Method Multiple Imputation Poisson Distribution Reproducibility Of Results Safety Self-controlled Case Series Vaccines Vaccine Safety

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• Source:
  Biom J. 60(4):748-760
• Pubmed ID:
  29768667
• Pubmed Central ID:
  PMC6589832
• Document Type:
  Journal Article
• Funding:
  CC999999/Intramural CDC HHS/United States ; UL1 RR025780/RR/NCRR NIH HHS/United States
• Volume:
  60
• Issue:
  4
• Collection(s):
  CDC Public Access
• Main Document Checksum:
  urn:sha256:4fef2380a4c39510b36dd05626f48ddce1d939c44c1a2d62a4d1557edaa664d3
• Download URL:
  https://stacks.cdc.gov/view/cdc/79380/cdc_79380_DS1.pdf
• File Type:
  [PDF - 412.09 KB ]