| アブストラクト | Background: Routinely collected healthcare data such as administrative claims and electronic health records (EHR) can complement clinical trials and spontaneous reports to detect previously unknown risks of vaccines, but uncertainty remains about the behavior of alternative epidemiologic designs to detect and declare a true risk early. Methods: Using three claims and one EHR database, we evaluate several variants of the case-control, comparative cohort, historical comparator, and self-controlled designs against historical vaccinations using real negative control outcomes (outcomes with no evidence to suggest that they could be caused by the vaccines) and simulated positive control outcomes. Results: Most methods show large type 1 error, often identifying false positive signals. The cohort method appears either positively or negatively biased, depending on the choice of comparator index date. Empirical calibration using effect-size estimates for negative control outcomes can bring type 1 error closer to nominal, often at the cost of increasing type 2 error. After calibration, the self-controlled case series (SCCS) design most rapidly detects small true effect sizes, while the historical comparator performs well for strong effects. Conclusion: When applying any method for vaccine safety surveillance we recommend considering the potential for systematic error, especially due to confounding, which for many designs appears to be substantial. Adjusting for age and sex alone is likely not sufficient to address differences between vaccinated and unvaccinated, and for the cohort method the choice of index date is important for the comparability of the groups. Analysis of negative control outcomes allows both quantification of the systematic error and, if desired, subsequent empirical calibration to restore type 1 error to its nominal value. In order to detect weaker signals, one may have to accept a higher type 1 error. |
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| 投稿者 | Schuemie, Martijn J; Arshad, Faaizah; Pratt, Nicole; Nyberg, Fredrik; Alshammari, Thamir M; Hripcsak, George; Ryan, Patrick; Prieto-Alhambra, Daniel; Lai, Lana Y H; Li, Xintong; Fortin, Stephen; Minty, Evan; Suchard, Marc A |
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| 組織名 | Observational Health Data Sciences and Informatics, New York, NY, United States.;Observational Health Data Analytics, Janssen R&D, Titusville, NJ, United States.;Department of Biostatistics, University of California, Los Angeles, Los Angeles,;CA, United States.;Quality Use of Medicines and Pharmacy Research Centre, Clinical and Health;Sciences, University of South Australia, Adelaide, SA, Australia.;School of Public Health and Community Medicine, Institute of Medicine,;Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.;College of Pharmacy, Prince Sattam Bin Abdulaziz University, Riyadh, Saudi;Arabia.;Department of Biomedical Informatics, Columbia University, New York, NY, United;States.;Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United;Kingdom.;Department of Medical Informatics, Erasmus University Medical Center, Rotterdam,;Netherlands.;O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary,;Calgary, AB, Canada.;Division of Medical Sciences, University of Manchester, Manchester, United;Department of Human Genetics, University of California, Los Angeles, Los Angeles, |
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