Type I error probability spending for post-market drug and vaccine safety surveillance with binomial data.

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.

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

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.

Faulty measurement substitution and control reconfiguration by using a multivariate flow control loop.

A two-tank multivariate loop was designed and built to support research related to instrumentation and control, equipment and sensor monitoring. This test bed provides the framework necessary to investigate and test control strategies and fault detection methods applicable to sensors, equipment, and actuators, and was used to experimentally develop and demonstrate a fault-tolerant control strategy using six correlated variables in a single-tank configuration. This work shows the feasibility of using data-based empirical models to perform fault detection and substitute faulty measurements with predictions and to perform control reconfiguration in the presence of actuator failure in a real system. These experiments were particularly important because they offered the opportunity to prove that a system, such as the multivariate control loop, could survive degraded conditions, provided the empirical models used were accurate and representative of the process dynamics.