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Post-approval safety surveillance of medical products using observationalhealthcare data can help identify safety issues beyond those found inpre-approval trials. When testing sequentially as data accrue, maximumsequential probability ratio testing (MaxSPRT) is a common approach tomaintaining nominal type 1 error. However, the true type 1 error may stilldeviate from the specified one because of systematic error due to theobservational nature of the analysis. This systematic error may persist evenafter controlling for known confounders. Here we propose to address this issueby combing MaxSPRT with empirical calibration. In empirical calibration, weassume uncertainty about the systematic error in our analysis, the source ofuncertainty commonly overlooked in practice. We infer a probabilitydistribution of systematic error by relying on a large set of negativecontrols: exposure-outcome where no causal effect is believed to exist.Integrating this distribution into our test statistics has previously beenshown to restore type 1 error to nominal. Here we show how we can calibrate thecritical value central to MaxSPRT. We evaluate this novel approach usingsimulations and real electronic health records, using H1N1 vaccinations duringthe 2009-2010 season as an example. Results show that combining empiricalcalibration with MaxSPRT restores nominal type 1 error. In our real-worldexample, adjusting for systematic error using empirical calibration has alarger impact than, and hence is just as essential as, adjusting for sequentialtesting using MaxSPRT. We recommend performing both, using the method describedhere.

Adjusting for both sequential testing and systematic error in safety surveillance using observational data: Empirical calibration and MaxSPRT