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Type I error probability spending for post–market drug and vaccine safety surveillance with binomial data
Author(s) -
Silva Ivair R.
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7504
Subject(s) - negative binomial distribution , null hypothesis , type i and type ii errors , binomial distribution , postmarketing surveillance , sample size determination , clinical trial , computer science , medicine , statistics , econometrics , mathematics , adverse effect , poisson distribution
Type I error probability spending functions are commonly used for designing sequential analysis of binomial data in clinical trials, but it is also quickly emerging for near–continuous sequential analysis of post–market drug and vaccine safety surveillance. It is well known that, for clinical trials, when the null hypothesis is not rejected, it is still important to minimize the sample size. Unlike in post–market drug and vaccine safety surveillance, that is not important. In post–market safety surveillance, specially when the surveillance involves identification of potential signals, the meaningful statistical performance measure to be minimized is the expected sample size when the null hypothesis is rejected. The present paper shows that, instead of the convex Type I error spending shape conventionally used in clinical trials, a concave shape is more indicated for post–market drug and vaccine safety surveillance. This is shown for both, continuous and group sequential analysis.