Sequential monitoring using the Second Generation P-Value with Type I error controlled by monitoring frequency
Jonathan J. Chipman (1, 2), Robert A. Greevy Jr. (3), Lindsay, Mayberry (4), Jeffrey D. Blume (5) ((1) Division of Biostatisitics,, University of Utah Intermountain Healthcare Department of Population Health, Sciences, University of Utah, (2) Cancer Biostatistics

TL;DR
This paper introduces SeqSGPV, a sequential monitoring method for scientific relevance that controls Type I error through monitoring frequency and affirmation, applicable to various hypotheses including PRISM, ROPE, and ROE.
Contribution
It formalizes sequential SGPV monitoring, introduces the PRISM hypothesis, and develops a novel error control method using monitoring frequency and affirmation steps.
Findings
SeqSGPV reduces wait time and sample size in simulations.
It effectively controls Type I error in various hypotheses.
Demonstrated applicability in real-world trial data.
Abstract
Many adaptive monitoring schemes adjust the required evidence toward a hypothesis to control Type I error. This shifts focus away from determining scientific relevance with an uncompromised degree of evidence. We propose sequentially monitoring the Second Generation P-Value (SGPV) on repeated intervals until establishing evidence for scientific relevance (SeqSGPV). SeqSGPV encompasses existing strategies to monitor Region of Practical Equivalence (ROPE) or Region of Equivalence (ROE) hypotheses. Hence, our focus is to formalize sequential SGPV monitoring; establish a novel set of scientific hypotheses, called PRISM, which is a ROE with a ROPE surrounding the null hypothesis; and use monitoring frequency and a novel affirmation step to control Type I error. Under immediate and delayed outcomes, we assess finite and limiting SeqSGPV operating characteristics when monitoring PRISM, ROPE,…
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Taxonomy
TopicsMeta-analysis and systematic reviews · Explainable Artificial Intelligence (XAI) · Statistical Methods in Clinical Trials
