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Type I Error Probability Spending for Post-Market Drug and Vaccine Safety Surveillance With Poisson Data

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  • Ivair R. Silva

    (Federal University of Ouro Preto)

Abstract

Statistical sequential hypothesis testing is meant to analyze cumulative data accruing in time. The methods can be divided in two types, group and continuous sequential approaches, and a question that arises is if one approach suppresses the other in some sense. For Poisson stochastic processes, we prove that continuous sequential analysis is uniformly better than group sequential under a comprehensive class of statistical performance measures. Hence, optimal solutions are in the class of continuous designs. This paper also offers a pioneer study that compares classical Type I error spending functions in terms of expected number of events to signal. This was done for a number of tuning parameters scenarios. The results indicate that a log-exp shape for the Type I error spending function is the best choice in most of the evaluated scenarios.

Suggested Citation

  • Ivair R. Silva, 2018. "Type I Error Probability Spending for Post-Market Drug and Vaccine Safety Surveillance With Poisson Data," Methodology and Computing in Applied Probability, Springer, vol. 20(2), pages 739-750, June.
    • Handle: RePEc:spr:metcap:v:20:y:2018:i:2:d:10.1007_s11009-017-9586-z
    DOI: 10.1007/s11009-017-9586-z
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    References listed on IDEAS

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    1. I. R. Silva & M. Kulldorff, 2015. "Continuous versus group sequential analysis for post‐market drug and vaccine safety surveillance," Biometrics, The International Biometric Society, vol. 71(3), pages 851-858, September.
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