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Adjusting for both sequential testing and systematic error in safety surveillance using observational data: Empirical calibration and MaxSPRT.
Schuemie, Martijn J; Bu, Fan; Nishimura, Akihiko; Suchard, Marc A.
Afiliação
  • Schuemie MJ; Observational Health Data Analytics, Janssen Research & Development, Titusville, New Jersey.
  • Bu F; Department of Biostatistics, University of California, Los Angeles, California.
  • Nishimura A; Department of Biostatistics, University of California, Los Angeles, California.
  • Suchard MA; Department of Human Genetics, University of California, Los Angeles, California.
Stat Med ; 42(5): 619-631, 2023 02 28.
Article em En | MEDLINE | ID: mdl-36642826
ABSTRACT
Post-approval safety surveillance of medical products using observational healthcare data can help identify safety issues beyond those found in pre-approval trials. When testing sequentially as data accrue, maximum sequential probability ratio testing (MaxSPRT) is a common approach to maintaining nominal type 1 error. However, the true type 1 error may still deviate from the specified one because of systematic error due to the observational nature of the analysis. This systematic error may persist even after controlling for known confounders. Here we propose to address this issue by combing MaxSPRT with empirical calibration. In empirical calibration, we assume uncertainty about the systematic error in our analysis, the source of uncertainty commonly overlooked in practice. We infer a probability distribution of systematic error by relying on a large set of negative controls exposure-outcome pairs where no causal effect is believed to exist. Integrating this distribution into our test statistics has previously been shown to restore type 1 error to nominal. Here we show how we can calibrate the critical value central to MaxSPRT. We evaluate this novel approach using simulations and real electronic health records, using H1N1 vaccinations during the 2009-2010 season as an example. Results show that combining empirical calibration with MaxSPRT restores nominal type 1 error. In our real-world example, adjusting for systematic error using empirical calibration has a larger impact than, and hence is just as essential as, adjusting for sequential testing using MaxSPRT. We recommend performing both, using the method described here.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Vírus da Influenza A Subtipo H1N1 Tipo de estudo: Prognostic_studies / Screening_studies Limite: Humans Idioma: En Revista: Stat Med Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Vírus da Influenza A Subtipo H1N1 Tipo de estudo: Prognostic_studies / Screening_studies Limite: Humans Idioma: En Revista: Stat Med Ano de publicação: 2023 Tipo de documento: Article