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

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

SUBMITTER: Silva IR 

PROVIDER: S-EPMC6936745 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature