Bounded-width confidence interval following optimal sequential analysis of adverse events with binary data

• Published in: Statistical methods in medical research Vol. 31; no. 12; p. 2323
• Main Authors: Silva, Ivair R, Zhuang, Yan
• Format: Journal Article
• Language: English
• Published: England 01.12.2022
• Subjects: Clinical Trials, Phase III as Topic; Confidence Intervals; Influenza A Virus, H1N1 Subtype; Influenza Vaccines - adverse effects; Probability; Sample Size; Vaccines - adverse effects; expected time to signal; Estimate precision; clinical trials; coverage probability; linear programming; post-market safety surveillance
• ISSN: 1477-0334
• DOI: 10.1177/09622802221122383

In sequential testing with binary data, sample size and time to detect a signal are the key performance measures to optimize. While the former should be optimized in Phase III clinical trials, minimizing the latter is of major importance in post-market drug and vaccine safety surveillance of adverse events. The precision of the relative risk estimator on termination of the analysis is a meaningful design criterion as well. This paper presents a linear programming framework to find the optimal alpha spending that minimizes expected time to signal, or expected sample size as needed. The solution enables (a) to bound the width of the confidence interval following the end of the analysis, (b) designs with outer signaling thresholds and inner non-signaling thresholds, and (c) sequential designs with variable Bernoulli probabilities. To illustrate, we use real data on the monitoring of adverse events following the H1N1 vaccination. The numerical results are obtained using the package.