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Factors Influencing Background Incidence Rate Calculation: Systematic Empirical Evaluation Across an International Network of Observational Databases.
Ostropolets, Anna; Li, Xintong; Makadia, Rupa; Rao, Gowtham; Rijnbeek, Peter R; Duarte-Salles, Talita; Sena, Anthony G; Shaoibi, Azza; Suchard, Marc A; Ryan, Patrick B; Prieto-Alhambra, Daniel; Hripcsak, George.
  • Ostropolets A; Columbia University Medical Center, New York, NY, United States.
  • Li X; Centre for Statistics in Medicine, NDORMS, University of Oxford, Oxford, United Kingdom.
  • Makadia R; Janssen Research and Development, Titusville, NJ, United States.
  • Rao G; Janssen Research and Development, Titusville, NJ, United States.
  • Rijnbeek PR; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
  • Duarte-Salles T; Fundacio Institut Universitari per a la Recerca a L'Atencio Primaria de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain.
  • Sena AG; Janssen Research and Development, Titusville, NJ, United States.
  • Shaoibi A; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, Netherlands.
  • Suchard MA; Janssen Research and Development, Titusville, NJ, United States.
  • Ryan PB; Department of Biostatistics, Fielding School of Public Health, University of California, Los Angeles, Los Angeles, CA, United States.
  • Prieto-Alhambra D; Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California, Los Angeles, Los Angeles, CA, United States.
  • Hripcsak G; Columbia University Medical Center, New York, NY, United States.
Front Pharmacol ; 13: 814198, 2022.
Article in English | MEDLINE | ID: covidwho-1952516
ABSTRACT

Objective:

Background incidence rates are routinely used in safety studies to evaluate an association of an exposure and outcome. Systematic research on sensitivity of rates to the choice of the study parameters is lacking. Materials and

Methods:

We used 12 data sources to systematically examine the influence of age, race, sex, database, time-at-risk, season and year, prior observation and clean window on incidence rates using 15 adverse events of special interest for COVID-19 vaccines as an example. For binary comparisons we calculated incidence rate ratios and performed random-effect meta-analysis.

Results:

We observed a wide variation of background rates that goes well beyond age and database effects previously observed. While rates vary up to a factor of 1,000 across age groups, even after adjusting for age and sex, the study showed residual bias due to the other parameters. Rates were highly influenced by the choice of anchoring (e.g., health visit, vaccination, or arbitrary date) for the time-at-risk start. Anchoring on a healthcare encounter yielded higher incidence comparing to a random date, especially for short time-at-risk. Incidence rates were highly influenced by the choice of the database (varying by up to a factor of 100), clean window choice and time-at-risk duration, and less so by secular or seasonal trends.

Conclusion:

Comparing background to observed rates requires appropriate adjustment and careful time-at-risk start and duration choice. Results should be interpreted in the context of study parameter choices.
Keywords

Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews / Systematic review/Meta Analysis Topics: Vaccines Language: English Journal: Front Pharmacol Year: 2022 Document Type: Article Affiliation country: Fphar.2022.814198

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Observational study / Prognostic study / Randomized controlled trials / Reviews / Systematic review/Meta Analysis Topics: Vaccines Language: English Journal: Front Pharmacol Year: 2022 Document Type: Article Affiliation country: Fphar.2022.814198